Alice Thouma Hatem

Marie-Thérèse Baddour

A special thanks to my fellow AlliansTic

Elizabeth Colin, for their continuous support, patience, motivation and immense knowledge.

Their guidance and dedication to research have been a real inspiration for me all along. I could not hope better advisors and mentors for my PhD.

Dr. Sara Abou Chakra, thanks for all the support, scientific knowledge and advice you provided me and all what you have taught me during my thesis. Thank you for the trust you gave me during the four years. Thanks for learning me to do everything in a perfect and right way. I will keep following your advice in my life.

Dr. Elizabeth Colin, thanks for your trust in me, the scientific discussions and remarks all over the years of my thesis. I will never forget your advices and the great interactions by treating me like your son. You inspired me the passion in research and patience in learning.

My sincere thanks go also to Altair Company and specially to Dr. Eddy Jehamy for the stimulating discussions and the sleepless nights. He has guided me to get impressive simulation results. Without his precious support, it would not have been possible to conduct this research.

Introduction

De plus en plus de services nécessitent des informations de localisation pour satisfaire aux besoins des utilisateurs. Le système de positionnement global par satellite (Global Positioning System GPS) qui apporte ces données dans les environnements extérieurs (Outdoor) est désormais généralisé. Les performances de ce système sont devenues excellentes et permettent d'atteindre des précisions de l'ordre de quelques mètres.

Le besoin de se localiser dans tous types d'environnements est devenu essentiel dans diverses applications, par exemple pour trouver des objets perdus ou pour aider les personnes âgées dans le cadre de leurs activités quotidiennes. On peut aussi utiliser ces systèmes dans le domaine médical pour détecter la position des patients dans les hôpitaux, ou celle des véhicules dans les parcs de stationnement.

Cependant, la détection des positions des personnes et des objets dans les environnements intérieurs (Indoor) présente une problématique toujours d'actualité vu que le système GPS fonctionne en mode dégradé, ou ne fonctionne pas en indoor. En effet, la localisation indoor est particulièrement difficile pour plusieurs raisons : la présence de multitrajets, l'importance de la visibilité indirecte (None Line-of-Sight NLoS), l'atténuation et les évanouissements des signaux à cause de la présence d'obstacles.

Des travaux de recherche sont menés aujourd'hui pour concevoir des systèmes de positionnement indoor (Indoor Positioning System, IPS) avec une grande précision, un coût raisonnable et une adaptation à des environnements et des scénarios multiples. Souvent, un certain compromis entre la précision, la couverture, le temps de latence, le coût et la robustesse, s'impose.

Les systèmes de localisation indoor peuvent être classés en systèmes exogènes ou endogènes. Cette classification est principalement basée sur l'infrastructure disponible qui peut être utilisée pour fournir des informations servant à la localisation. Cependant, dans des environnements complexes, l'infrastructure déjà existante ne peut pas être efficace pour des précisions élevées. D'où la nécessité de développer des systèmes de positionnement dédiés ou endogènes.

viii Au prix d'une main d'oeuvre et de temps de déploiement importants, les solutions endogènes sont plus précises. Pour cette raison, nous nous intéressons au développement d'une solution de type endogène, afin d'améliorer la précision de localisation dans différents scénarios indoor.

Le reste de ce résumé sera élaboré comme suit : la section 2 présente les différentes métriques et techniques utilisées dans les systèmes de localisation indoor. Les différentes technologies seront détaillées dans la section 3. La section 4 définit les limitations des systèmes de localisation existants et les objectifs de notre thèse. Les systèmes de localisation conventionnels et le système que nous utilisons comme base seront décrits dans la section 5.

La section 6 détaille la modélisation de l'environnement de test et du matériel RFID. Le système complet proposé, ainsi que toutes les améliorations apportées sont détaillés et évalués dans la section 7. Le concept de constellation de balises, appliqué dans notre système de localisation, est ensuite introduit dans la section 8. Les résultats obtenus sont synthétisés en conclusion, section 9. La puissance du signal reçu (RSS) présente une grande simplicité et une large utilisation en localisation indoor, grâce à son accessibilité dans la plupart des dispositifs de communication. En revanche, la majorité des systèmes de localisation indoor, utilisant des techniques basées sur le RSS, restent de faible précision et de stabilité limitée à cause de la présence de multi-trajets et de la visibilité directe réduite. ix Les systèmes de localisation indoor utilisent différentes techniques pour déterminer la position de la cible, telles que : la proximité, la triangulation, la multi/trilatération et le fingerprinting. La technique de proximité présente une grande simplicité d'implémentation et dépend de la densité de balises déployées dans l'environnement considéré. Elle consiste à signaler que la cible est dans la zone de couverture d'au moins une balise. La technique de triangulation utilise des propriétés géométriques pour déterminer la position de la cible, en se servant de plusieurs antennes de référence. Cette approche utilise généralement différentes métriques telles que RSS, ToA, TDoA et AoA.

Métriques et techniques de localisation indoor

La multi/trilatération quant à elle, est basée sur l'estimation de distances balises-cible ; la position de la cible étant le point d'intersection des cercles/sphères de rayons correspondants aux distances estimées. La trilatération utilise trois balises fixes de position connue pour la détection de la position de la cible et permet une localisation en deux dimensions (2D). En revanche, la multilatération nécessite plus de trois balises fixes, pour localiser un objet et peut permettre une localisation en trois dimensions (3D).

La technique de fingerprinting est une méthode très employée. Elle est composée de deux étapes : hors ligne (offline) et en ligne (online). Pendant la phase hors ligne ou phase de calibrage, les puissances reçues sont enregistrées pour créer une carte radio (radio map) de l'environnement. La fiabilité de cette phase exige des ressources humaines et du temps pour récupérer les informations de puissance. Durant la phase en ligne, la position de la cible est estimée en faisant correspondre la puissance reçue avec la base de données de la carte radio à l'aide de méthodes probabilistes ou d'apprentissage.

Ces quatre techniques sont appliquées dans différents scénarios de localisation indoor et présentent l'avantage d'être simples à mettre en oeuvre pour une précision de localisation variable. En revanche, ces techniques ont des limitations dues entre autres à l'influence des obstacles ou les effets des multi-trajets.

Technologies de localisation

Plusieurs technologies sont employées par les systèmes de localisation indoor. Ces technologies sont divisées en plusieurs catégories : la vision, la navigation inertielle, les ondes acoustiques et les ondes électromagnétiques. Dans le cadre de cette thèse, nous nous concentrerons essentiellement sur les technologies Radiofréquence (RF) omniprésentes et peu coûteuses. Contrairement aux systèmes de localisation basés sur la vision, les ondes électromagnétiques se propagent dans l'environnement permettant la localisation dans des x scénarios sans visibilité directe (NLoS). Grâce à cette caractéristique, les systèmes de positionnement RF présentent une large zone de couverture et nécessitent moins de matériel que les autres systèmes.

Les technologies hertziennes telles que ZigBee, Bluetooth, WiFi, Ultra-Wide Band ou RFID, sont couramment implémentées dans la plupart des objets connectés. Les trois premières technologies fonctionnent dans la bande de 2,4 GHz et leur précision de positionnement est affectée par les effets des multi-trajets dominants dans les environnements indoor. Vu que la technologie RFID active apporte des avantages pertinents, plus précisément le coût et la grande zone de couverture, nous avons choisi de l'utiliser pour notre système de localisation. Les problématique et les objectifs de notre travail seront détaillés dans la section suivante.

Problématiques et Objectifs

Plusieurs inconvénients entravent le développement des systèmes de localisation RFID.

Ces systèmes, implémentés souvent avec des techniques de proximité, deviennent coûteux lorsqu'un grand nombre de balises et de lecteurs RFID sont déployés dans l'environnement.

De plus, le déploiement d'un grand nombre de balises augmente les interférences. Par ailleurs, la variabilité dans la propagation des signaux est un problème courant dans la plupart des systèmes de localisation RFID basés sur le RSS. Cette instabilité est due à la non-stationnarité du canal, ce qui affecte la précision de la localisation.

xii Les recherches pour proposer des solutions de localisation abondent, mais les implémentations de ces solutions, à coût réduit, se font rares. Dans ce contexte, l'objectif de cette thèse est d'introduire un nouveau système de localisation avec la technologie RFID, offrant un coût bas, ainsi qu'une fiabilité, stabilité et précision élevées. Par conséquent, trois objectifs principaux sont définis : améliorer le calibrage, optimiser la densité des balises et augmenter la précision de la localisation (Figure R.1).

Pour ce faire, cinq axes de recherche principaux sont menés. La première contribution est de modéliser de manière fiable deux environnements intérieurs, ce qui permet de remplacer de longues compagnes de mesure. De plus, l'estimateur de maximum de vraisemblance L'estimation de distance est obtenue en appliquant le modèle de propagation avec les paramètres correspondant à l'environnement. Enfin, la position du lecteur RFID est estimée en appliquant la technique de multilatération.

Modélisation de l'environnement et du système RFID

Système de localisation RFID proposé

Dans cette section, une meilleure technique de combinaison des signaux acquis, deux modèles de propagation et une nouvelle méthode de calibrage seront introduits.

Combinaison des RSS

Les acquisitions de RSS, récupérées durant la phase hors ligne (offline) et en ligne (online), sont combinées par le biais de l'estimateur de maximum de vraisemblance (MLE). Le MLE des puissances mesurées est défini par

𝑅𝑅𝑅𝑅𝑅𝑅 𝑀𝑀𝑀𝑀𝑀𝑀 = 𝑎𝑎𝑎𝑎𝑎𝑎 𝑚𝑚𝑎𝑎𝑚𝑚 𝑝𝑝(𝑅𝑅𝑅𝑅𝑅𝑅 𝐾𝐾 ) (R.2)
Avec, argmax est l'opérateur qui donne la valeur de RSS la plus probable.

Modèles de propagation

Méthode de calibrage

Après l'introduction des deux modèles de propagation indoor, l'optimisation de notre système de localisation consiste à améliorer la fiabilité du calibrage et la précision de la localisation.

Nous proposons un

𝑛𝑛 𝑤𝑤1 = � 𝑛𝑛 𝑇𝑇 𝑖𝑖 7 𝑇𝑇=1 𝑁𝑁′𝑃𝑃𝑃𝑃𝑃𝑃 𝑇𝑇 𝑖𝑖 � 𝑁𝑁′ 𝑇𝑇𝑃𝑃𝑇𝑇𝑇𝑇𝑇𝑇 (R.5) Pour d > 3 ⎩ ⎪ ⎪ ⎪ ⎨ ⎪ ⎪ ⎪ ⎧ 𝐴𝐴 𝑤𝑤2 = � 𝑎𝑎 𝑇𝑇 𝑖𝑖
𝑃𝑃 𝑟𝑟 (d) = � 𝑃𝑃 0 -10. n w 1 . log 10 (d)+X w 1 d ≤ 3λ 𝑃𝑃 0 ′ -A w 2 . log 10 (d) 2 -B w 2 . log 10 (d) -C w 2 d > 3λ (R.

Conclusion

List of Tables

Introduction

The Global Positioning System (GPS) already provides a satisfactory solution for outdoor localization. Extending that service to indoor environments is still limited due to many substantial challenges. Thus, an accurate indoor localization approach is important and needed for different applications. Among these applications, we can mention public safety, commercial and security domains. For instance, indoor localization solutions can assist the elderly in their homes as part of their daily activities. They can play a significant role in the medical field and are recommended for police and firefighters. However, none of the existing localization

services have yet accurately localized people in indoor environments with low cost and complexity solutions.

Intense research works are carried today to design Indoor Localization Systems (ILS).

Many technologies have been considered, e.g. ultrasonic, InfraRed (IR), vision, and Radio The third axis is focused on introducing new empirical indoor propagation models associated with a new calibration approach determining Weighted Average Attenuation Factors (WAAF).

Finally, referring to the Multiple Inputs Single Output (MISO) approach, the concept of using a group of active RFID tags instead of a single one is analyzed to enhance the location accuracy thanks to signals' diversity. We have named this group of tags as a "Constellation". The size, shape, and the number of tags in the constellation are main keys for improving the location accuracy.

Work is summarized as follows: Chapter 5 suggests a Multiple Inputs Single Output system (MISO) approach to define the concept of constellation of RFID tags used for indoor positioning. The best shape, radius, and the optimal number of tags in the constellation are studied. Then, the position errors obtained with the proposed localization system, performing with the optimal constellation of tags, are analyzed and compared with those obtained with the single tag architecture.

After a comparative analysis regarding the density of tags deployed, the last part is dedicated to summarize all gathered results and give conclusions. Recommendations are elaborated to open perspectives for future work.

Chapter 1 State of the Art Introduction

Locating people or devices in a given area appeared a few years ago and has become an essential element of the contextual information [1]. Due to the excellent performance of the Global Positioning System (GPS) in outdoor environments, determining positions of people indoors such as buildings, houses, warehouses, airports and industries has also become highly needed. Moreover, this aim has been largely boosted by the widespread use of wireless communications.

Recently, indoor localization has witnessed large interest due to the wide range of potential services provided by the Internet of Things (IoT) and ubiquitous connectivity.

Different technologies and techniques are introduced to provide indoor localization services to the end-users; they will be expanded in detail in this chapter.

In addition, each Indoor Positioning System (IPS) performs some specific characteristics such availability, cost, range, latency, scalability and location accuracy, depending on the application and the considered indoor environment [2]. Systems found in the literature will be highlighted and assessed upon their performance and limitations.

In this context, Radio Frequency (RF) based localization techniques become increasingly popular as they offer pervasive and low-cost solutions, as discussed in [3]. In characteristics of an Indoor Localization System (ILS) followed by existing ILS as well as their efficiency and drawbacks are defined and discussed in section 1.5. Finally, the chapter ends by a detailed conclusion and motivations in section 1.6.

Overview of Positioning Metrics

Despite a large number of different RF positioning systems and solutions, there are very few forms of metrics used to detect the user's location. such as: These metrics can be broadly This section presents the most popular positioning metrics used to locate a person or object in an indoor environment i.e. ToA, TDoA, AoA and RSS.

Time of Arrival

The The typical signals' propagation time from the transmitting to the receiving antennas is about tens of nanoseconds in an indoor environment. Therefore, the ToA approach requires strict synchronization between Tx and Rx [4].

Time Difference of Arrival

The Time Difference of Arrival (TDoA) principle is focused on estimating the receiving antenna's location by determining the difference in time at which the signal is received from different transmitting antennas [5]. Thus, time difference is, in this case, sufficient to estimate the distance between the transmitter and receiver, instead of knowing the time of transmission.

This method requires at least three transmitting antennas for each TDoA measurement to detect the receiver's position as the intersection of three (or more) hyperboloids.

The 2D model, illustrated in Figure 1.2 presents the intersection of the hyperboloids generated by all transmitting antennas to determine the receiver's location.

Figure 1.2 TDoA-based localization

Like the ToA, TDoA requires LoS to mitigate multipath that affect the location accuracy. Furthermore, synchronization is also mandatory in order to estimate the receiver's location accurately. But unlike the ToA technique, synchronization in TDoA is only required between the transmitting antennas (Tx), since the receiver's location is determined based on time or distance difference between them. The TDoA technique presents higher location accuracy compared to the ToA [6].

Angle of Arrival

Angle of Arrival (AoA) uses the angle at which the signal is received from a transmitting antenna. In the case of Angle of Departure (AoD), the transmitting antenna needs to send its absolute coordinates to the receiving antenna. With AoA, the transmitting antenna determines a directional line from its location to estimate different positions of the receiving antenna. The location of the receiver is then the intersection of several lines from several transmitting antennas (Figure 1.3).

Figure 1.3 Positioning based on AoA measurement

At least two fixed transmitting antennas (Tx 1 , Tx 2 ) with the two corresponding angles are used to determine the receiver's location (Rx) in the two dimensions space (2D). This method can also be extended into 3D, using at least three transmitting antennas.

AoA doesn't need time synchronization between transmitting (Tx) and receiving (Rx)

antennas. However, although the need of complex hardware, the receiving antenna's location accuracy reduces with the increase of the transmitter-receiver distance.

Finally, the transmitter-receiver distance can also be estimated through PoA and PDoA.

These two metrics are based on the phase or phase difference. It requires the pure emission of sinusoidal signals from transmitting antennas [1].

Received Signal Strength

The Received Signal Strength Indicator (RSSI) or the Received Signal Strength (RSS)

is the simplest and the widely used approach for indoor localization due to the availability of the RSS data in most end-user devices [1]. RSS is used for distances estimation. The distance between the transmitting and receiving antennas is estimated based on RSS by converting the received power into distance. Then, a classical technique like the trilateration can be applied to detect the receiver's location (Rx) position, as illustrated in Figure 1.4.

Figure 1.4 RSSI-based localization

The improvement of the location accuracy depends on the density of antennas deployed in the considered environment. Unlike ToA and TDoA, this method does not need time synchronization between the transmitting and receiving antennas.

In outdoor scenarios, RSS values are conversely proportional to the square of the transmitter-receiver distance. The RSS metric provides high location accuracy thanks to the dominance of LoS signal propagation. However, the propagated signals are affected and obstructed indoors due to shadowing and several multipath fading, making this positioning metric less accurate for distances estimation, compared to other metrics presented previously [8]. Moreover, there is no unique solution based on theoretical and empirical propagation models that could be used to avoid multipath effects. Hence, the fingerprinting method is introduced to build a radio map reflecting the real environmental impacts and investigating signals' uncertainties.

Summary for Positioning Metrics

Advantages and defaults of the different metrics used for localization purposes are summarized in the Table 1.1. Overall, the RSS positioning metric is mostly effective for ILS thanks to the availability of RSS data in most end-user devises such as smart phones. However, its high sensitivity to multipath affects the accuracy significantly.

Table 1.1 Comparison of positioning Metrics

Metric

Overview of Positioning Techniques

This section gives an overview of fundamental principles for positions determination, from various basic datasets, such as proximity, triangulation, multi/trilateration and fingerprinting observations.

Proximity

The proximity technique can be categorized as of three types [9]. The first one is detecting physical contact. The second one is monitoring antennas to locate the target if it is in the range of one or more antennas. The third one is observing the identification of the labeled target such as public transport cards. Labels are usually a tag, button or barcode attached on the target.

The proximity technique needs a dense number of deployed antennas in the desired field to provide the location information. This approach is relatively simple to be implemented.

If only one antenna detects the target, it must be collocated with it. However, when more than one antenna detect the target, the antenna that receives the strongest signal, must be collocated with the target [10].

As in Figure 1.5, more than one reader are used, in this case the target receiving the antenna's location is defined by the intersection of these readers' coverage areas. Hence, a smaller range of the central node is recommended to improve the location accuracy.

Figure 1.5 Localization by Proximity

Triangulation

The triangulation approach uses the triangular geometric properties to determine the target position [11]. It is estimated by determining its distance from multiple reference antennas. This approach usually uses different positioning metrics to measure the distance between the transmitting antennas and the target object such as RSS, ToA, TDoA, AoA and AoD. The advantage of AoA and AoD approaches, compared to other metrics, is that the location estimation can be made only with two transmitters in 2D.

Multi/Trilateration

The term trilateration refers to the process that estimates the target position based on the distance from three known antennas [12]. After converting the RSS value to a length, each transmitter-receiver distance is represented by a circle with a radius around the fixed antenna

(Tx i ) in

Figure 1.7 The Multi/Trilateration Technique

The multilateration technique has the same concept as trilateration, but it requires more than three fixed known antennas to locate an object [13]. Using the Euclidean distance formula, the (Rx) position is estimated.

Fingerprinting

Researchers have focused on applying fingerprinting within indoor localization systems [14]. It is a popular method to estimate a target location. This technique involves two stages:

offline and online (Figure 1.8). During the offline stage, the collected RSSs are stored in order to build a radio map. The reliability of this stage requires more effort in terms of time and labor for the collection of fingerprints. Within the online stage, the target's location is estimated by matching the collected RSSs with the built database.

Figure 1.8 Fingerprinting

Fingerprinting can be based on either probabilistic or machine learning methods such as the K-nearest-neighbor (KNN), neural networks, Support Vector Machine (SVM), and Smallest M-vertex Polygon (SMP) [15].

The probabilistic approach stores the RSS measurements at every known position to build the probabilistic model, during the offline stage. Then, the online RSS observations are matched with the optimal RSS calculated during the offline process, to identify the target location [16].

KNN is one of the learning algorithms used by several IPSs. The target position is estimated according to the greatest similarity between the measured RSS and its k-nearest neighbors. The SVM algorithm is a more advanced learning algorithm that shows better localization accuracy with a higher computational cost. It is extensively used in medicine, engineering and science.

The SMP algorithm uses the online RSS values to detect the target's location by taking the location unit in which the RSS value is the closest to the observations. Then, it founds the nearest point within the closest boundary using the smallest distance function [17].

Any changes in the considered environment geometry or structure (walls and ceilings) as well as in furniture, modify the diffraction, reflection, and scattering; this leads to variations in the RSS spatial distribution, that doesn't correspond to the database build initially and increases the target location uncertainty. Hence, the need to update the radio map.

Overview of Positioning Technologies

In this section, technologies that have been used to provide indoor localization services will be presented and discussed. There is a wide variety of localization technologies such as vision, acoustic, ultrasound, light and Infrared (IR).

The camera or vision technology is performed in different way with fixed camera systems and mobile camera systems. The success of ILS based on optical methods comes from the improvement and miniaturization of devices and the advancement of smartphones [19].

Therefore, the location accuracy of the camera-based localization system is affected by the camera pose errors [20].

The acoustic technology shares many characteristics with Radio Frequency (RF). Three mutual challenges are faced in all acoustic-based localization systems: low Signal-to-Noise Ratio (SNR), multipath effects and the selection of speakers and microphones already embedded in smart devices [21].

The Ultrasound-based ILSs use quality control in manual assembly processes to detect the object's location [22]. This technology needs synchronization between the ultrasound emitters and receivers because the travelled distance is determined via the ToA method [23].

Concerning the light-based ILSs, they are cost-effective solutions with the popularity of Light Emitting Diode (LED). Most of the existing systems perform in 2D and fail to reach the target location in 3D [24]. This technology presents a fundamental limitation due to the NLoS between the LED and the target. It can provide a localization error less than 8 centimeters in a 25 square meters' room [25]. In the same context, Light Fidelity (LiFi) is a new wireless technology to provide the connectivity within network environments. It sends the data through LED lights with a high speed and low-cost. It also provides high security communications with large bandwidth [26].

In addition, [27] and Radio Frequency Identification (RFID) that will be adopted for our activity.

The following subsections present the characteristics such as cost, reliability, performance, confidentiality, coverage and the location accuracy for these radio communication technologies used within localization applications.

ZigBee

Referring to IEEE 802.15.4 standard, Zigbee provides low cost, low data rate and low power consumption [1]. But, it presents high maintenance cost. ZigBee usually uses the RSS values to estimate the target's position [28]. The signal coverage of a ZigBee node is up to 100 meters in free space, whereas it is typically between 20 and 30 meters indoors [29]. As this technology operates at 2.4 GHz in an Industrial, Scientific and Medical (ISM) radio frequency band, it may suffer from unintentional interference from coexisting radio devices [30].

Concerning ZigBee-based localization systems, [31] proposes an algorithm named Self-Calibrating Centroid Localization (SCCL) based on ZigBee. The optimal estimated position error achieved is less than 1 meter in an L-shaped 168 square meters' corridor. In the same context, [32] introduces a ZigBee-based ILS; in order to improve the location accuracy and mitigate the RSS fluctuations and the number of samples, a ZigBee Wireless Sensor Network (WSN) of regular variance and gradient data particle filter is used. The proposed system performs with low power consumption to achieve an average accuracy of

Bluetooth

Similar to ZigBee, Bluetooth is a Wireless Personal Area Network (WPAN) standard.

Yet, the Bluetooth Special Interest Group guides the proprietary specification of Bluetooth.

This technology shares information between devices with high security, low cost, low power consumption, and small size [34]. uncertainty between 2 and 3 meters as well [27].

With the emergence of the Bluetooth Low Energy (BLE) as the latest version of Bluetooth, data rate is improved to 24 Mega bit per second (Mbps) and the outdoor coverage to 70 to 100 meters with high energy efficiency [35]. Compared to the conventional Bluetooth, the BLE can perform with different positioning metrics such ToA, TDoA, AoA and RSS.

Although, most of the existing BLE-based ILS rely on the RSS positioning metric [1]. The target location is usually determined with the proximity method when the distance between the BLE and the user is less than 1 meter.

Figure 1.9 Typical architecture for BLE-based localization system [START_REF] Fazzolari | Indoor localization system based on Bluetooth low energy for museum applications[END_REF] According to the indoor localization literature, [2] investigates the use of Bluetooth wireless technology for positioning in different applications. [36] proposes a technique that uses the Monte Carlo localization (MCL) algorithm that exploits two sensors: accelerometer and compass, with commonly deployed BLE beacons. The average position accuracy achieved in a laboratory and an office space is less than 1 meter in LoS scenario, and 3 meters in NLoS environment. Besides, [37] intends to evaluate the accuracy of a BLE positioning system especially when multiple devices are used. That accuracy is improved by using the average or median of a certain number of RSS measurements instead of a single RSS value collected at the same spot. Furthermore, [START_REF] Sadowski | Optimization of BLE beacon density for RSSI-based indoor localization[END_REF] studies the optimal number of BLEs to enhance the localization accuracy, using ten beacons as transmitting devices. Two algorithms and two types of filtering are used to develop an accurate ILS. By placing six beacons in a 78.84 square meters' research lab, the optimal error achieved is 1.15 meters. Recently, [START_REF] Fazzolari | Indoor localization system based on Bluetooth low energy for museum applications[END_REF]

Ultra-Wide Band

Ultra-Wide Band (UWB) is a radio technology for short-range and large-bandwidth communications. In UWB, a sequence of ultra-short-pulses with a low duty cycle (1 nanosecond) is transmitted through a large bandwidth (greater than 500 MHz). This technology operates in the frequency range between 3.1 to 10.6 GHz, with low power consumption [1].

UWB is widely used for short-range communications, such as PC peripherals and other indoor applications [29]. For indoor positioning, UWB can be used because it presents a strong multipath immunity. To determine the target's location, the three different positioning metrics

ToA, TDoA and AoA can be used [2]. Thus, UWB-based localization systems exploit the feature of time synchronization with both ToA and TDoA metrics. However, by applying the AoA method, UWB-based ILSs perform at the expense of a high cost due to the need for enduser's hardware.

To date, several UWB localization systems have been listed. [START_REF] Hanssens | An indoor localization technique based on ultra-wideband AoD/AoA/ToA estimation[END_REF] proposes an ILSbased on UWB channel sounding. By combining the triangulation and the trilateration methods for ToA, AoD and AoA positioning metrics, the optimal location accuracy is 42 centimeters with LoS. Furthermore, [START_REF] Groβwindhager | SnapLoc: An ultra-fast UWB-based indoor localization system for an unlimited number of tags[END_REF] presents a fast ILS at a theoretically upper bound of 2.3 kHz. The proposed system uses the TDoA for distance estimation between anchors. It is implemented and evaluated experimentally on a low-cost platform based on the Decawave (DW1000) UWB radio. The positioning accuracy reached at 90% CDF is of 33.4 centimeters. Recently, [START_REF] Otim | Towards Sub-Meter Level UWB Indoor Localization Using Body Wearable Sensors[END_REF] shows an UWB-based ILS using a particle filter that mitigates the ranging error of human body shadowing. ToF metric is used to determine the location of the user. Based on simulations and measurements, performances of the proposed system are assessed in a laboratory of 78 square meters. Results show a reduction in the median position error of up to 69 and 77 centimeters, through simulations and experiments, respectively.

Radio Frequency IDentification

Radio Frequency IDentification (RFID) technology has received great attention in the last decade. Typical applications include baggage handling, supply chain, or fixed assets tracking [7]. It can operate at different frequency bands such as Low Frequency (LF) from 30

to 150 kHz, High Frequency (HF) from 3 to 30 MHz, Ultra-High Frequency (UHF) at 433

MHz and from 868 to 915 MHz, and Super High Frequency (SHF) from 2.4 to 2.5 GHz and 5.8 GHz [29].

RFID systems consist of readers and tags that communicate via an electromagnetic wave. Systems that operate from 30 kHz to 150 kHz and at 13.56 MHz, work in the near-field region where the distance traveled by the propagating signal is much less than its wavelength.

RFID systems operating in typical UHF and SHF frequency bands, perform in the far-field region where the distance traveled by the RF propagating signal is much greater than its wavelength [START_REF] Zhang | Real-time locating systems using active RFID for Internet of Things[END_REF]. Tag-reader coupling is inductive in near-field region (mainly at LF and HF) while it is radiative in the far-field region (UHF and SHF).

The RFID range is wide and varies in terms of the frequency, from few centimeters up to 200 meters, whether the deployed RFID tags are passive or active [START_REF] Ahson | RFID handbook: applications, technology, security, and privacy[END_REF].

Passive RFID

Passive RFID tags usually operate without battery, but an internal battery can be added to improve the system range. The passive tag consists of three parts: an antenna, a semiconductor chip attached to the antenna, and some form of encapsulation. The RFID reader is responsible for powering and communicating with the tag. The RFID tag antenna captures the energy. Then, load modulation allows to transfer the tag's ID to the reader. Passive RFID tags are much cheaper and smaller than the active ones [29]. However, in UHF and SHF bands, due to multipath fading or absorption by objects in the range of the reader, the readability of passive tags is severely affected [START_REF] Bhaskar | A dual band dual antenna with read range enhancement for UHF RFID tags[END_REF]. 1.2 lists the frequency categories and most common passive RFID system frequencies along with its communication range [START_REF] Ahson | RFID handbook: applications, technology, security, and privacy[END_REF]. In fact, the localization accuracy is an essential criterion. However, other ILS characteristics should be taken into consideration. They will be presented in the following section.

Indoor Localization Systems Characteristics

First of all, IPSs are generally classified into exogenous or endogenous [START_REF] Cullen | CAPTURE-Extending the scope of self-localization in Indoor Positioning Systems[END_REF], [START_REF] Cullen | CAPTURE-Widening the Net-Indoor Positioning using Cooperative Techniques[END_REF]. This classification is mainly based on the available infrastructure that can be used to establish locations information. For instance, WiFi infrastructures are available in the majority of indoor scenarios, and the cost of WiFi-based localization is low because no specific infrastructure is required. These exogenous IPSs were extensively studied in the past years. However, in the case of harsh indoor conditions, the already implemented infrastructure may not be efficient for high precisions. Hence, the need for developing tailored positioning systems.

Whereas, endogenous IPSs are made up of infrastructure not been installed in advanced.

This kind of solutions usually requires a comprehensive site survey, a significant manpower and time for deployment, as well as more hardware. However, it provides optimal positioning systems. In our activity, we are interested in the endogenous solution in the aim to enhance the localization accuracy in different indoor scenarios.

It is worth recalling that characteristics and performance of ILSs are application dependent. Hence, the main challenge is to define the ILS features that correspond to the application needs. Many aspects characterize each ILS and its performance [1]. Table 1.4

defines the main IPSs characteristics.

Table 1.4 Indoor Positioning Systems Characteristics

Characteristics Definition

Cost

The system should not incur any additional infrastructure (Exogenous solution) and does not require any complex end-user hardware.

Reception Range

An accurate location recommends a reasonable communication range that depends on the application and the environment.

Accuracy

The positioning system should limit the impact of multipath effects.

Latency

The Real Time Localization System (RTLS) requires the user location without any noticeable delay.

Scalability

The system should be able to simultaneously locate or provide services to a large number of users in a large space.

These aforementioned characteristics are significant in all localization systems evaluation. Each system depends on the corresponding application and the scale of deployment.

An ideal localization system should gratify all indoor applications. Yet, there is no such proposed system that satisfies all of these requirements. However, some systems, that have been proposed recently, insure most of these factors.

Finally, Table 1.5 summarizes all characteristics for each technology. In the case of reader-based localization, the RFID reader is usually attached to the tracked person or object while tags are installed in the environment at known locations [4]. The location of the reader is estimated through signals transmitted from tags. Accuracy and resolution of the position estimation are increased with a high density of tags deployed in the environment [5]. Hence, the system cost relatively increases with either active or passive RFID tags indoors.

Otherwise, tag-based localization systems allow to estimate positions of the RFID tags, that are placed on objects [6]. They are suitable for several applications, starting from locating goods in warehouses to tracking luggage in airports. They can provide the same location accuracy as that by reader-based localization structures.

Both architectures face several challenges. They will be described in the following section.

This chapter is organized as follows: Section 2.2 describes the major challenges facing most RFID-based localization systems along with solutions found in the literature. Section 2.3

presents the environment where our experiments will be conducted. The conventional architecture is presented in section 2.4. RFID equipment used are presented in section 2.5.

Finally, the chapter is summarized in section 2.6.

Drawbacks and Existing Solutions of UHF RFID based ILSs

Several challenges hinder further development of existing RFID-based IPSs. Some of the drawbacks faced in the ILSs are the system's cost, signals' interference, signals' variability and the computation complexity.

The system's cost relies on the large number of tags and readers to be deployed [7]. [11] proposes an approach to improve the LANDMARC algorithm; this RFIDbased ILS reaches an average estimated distance error of 75 centimeters in 50 square meters by deploying 4 RFID readers and 28 active RFID reference tags, operating at 433 MHz.

Moreover, VIRE method is applied to improve the localization performance based on LANDMARC. [12] adapts the VIRE solution by adopting an array formed by 8 active reference tags. One RFID reader is used to determine the location of the target tag. This approach is validated in 9 square meters through a simulated environment. The achieved average location error is 37 centimeters.

On the same way, [13] shows a novel hybrid system for indoor localization; both SA-LANDMARC and COCKTAIL algorithms were introduced within a tested area of 36 square meters. These two algorithms run in two phases. The first phase is SA phase, which stands for Sensor Assisted. The second phase is the localization phase. It uses information of all reference tags and the Support Vector Regression (SVR) to localize the object. Accuracy reached 70 and 45 centimeters respectively, using 49 active RFID tags, operating at 303.825 MHz. Despite the SA-LANDMARC's implementation simplicity and COCKTAIL's efficiency, the achieved high precision, using both algorithms, refers to the dense deployment of active RFID tags which is around one tag per square meter. After stating these references [10]- [13], it is verified that major RFID localization systems count on the number of tags. To overcome this issue, our proposed ILS presents an accurate and cost-efficient positioning solution by deploying a reduced number of active RFID tags.

Another drawback of existing RFID-based ILSs is the RF interference between reference tags. Moreover, the deployment of many reference tags indoors may interference between them [14]. To improve the detectability of the location target, while reducing the intertags interference, [15]

As additional default, large signals variability is a common anecdotal problem in most

ILSs based on the RSS positioning metric [17]. This instability is due to the propagation channel non-stationarity and multipath. Therefore, the need for assuring signals stability is very essential to mitigate the location uncertainty. An available solution is presented by [18]; it implements and assesses the D-Watch device using both the direct path and multipath to improve the location accuracy to the decimeter level. Another solution is proposed by [19]. It The RFID localization system reaches high accuracy of 1 meter, in the considered indoor environment and outperforms several systems based on fingerprinting. Hence, the use of advanced algorithms will be recommended. However, this increases the system's cost and complexity. Table 2.1 recapitulates the main RFID-based ILSs' drawbacks and given solutions.

Table 2.1 Summary of RFID-based ILSs' Drawbacks and Solutions

Drawbacks Proposed Solutions

High Cost

Introducing feasible algorithms to reduce the number of deployed RFID tags.

RF Interference

Integrating DR tags with the optimal large-scale MIMO technology.

Measuring the phase difference of reference RFID tags.

Signals' Variability

Implementation of a D-Watch device that uses both the direct path and multipath.

Use of an antenna array to find the strongest path.

Systems' complexity Introducing a deep learning algorithm

Given the RFID-based ILSs' drawbacks aforementioned, solutions found in the literature are still laborious and complex. They differ whether the RFID system is active or passive. A brief comparison of UHF RFID localization schemes surveyed is presented in Table 2.2, showing the frequency, localization algorithm, tags density and accuracy. Moreover, the system's accuracy is improved by proposing a reliable calibration approach associated with empirical indoor propagation models.

The proposed ILS is reader-based. It features locating people, with a positioning error equivalent to an individual step i.e. less than 1 meter. In the aim of covering a wide area, the tags need to have a long operation range. Hence, the proposed system operates at 433 MHz. In addition, this reduces the density of deployed tags.

Experimental Environment

Experiments were carried out in two different environments at the Engineering School EFREI-Paris: an unfurnished classroom on the fourth floor and a hall on the ground. However, the proposed localization system is assessed only in the classroom.

The environment has an area of approximately 63.75 square meters (8.5 x 7.5 square meters). Although unfurnished, this environment can still be considered complex for RF signals propagation due to its asymmetric geometry and specific structure. More precisely, the left wall presents some strengthening corners, and the right wall is full of glass. This classroom environment is also occupied by some unmovable metallic objects, including a very large heater (with 8 meters of length and 1 meter of height) with sharp blades, a metallic board on the back wall, an LCD projector on the ceiling, one fire detector fixed on the ceiling, and speakers fixed on the ceiling and also on the back wall. The four views of the classroom are shown in (Figure 2.1).

Figure 2.1 Indoor site of experiments in the classroom

The use of AutoCAD is helpful to design the indoor environment with small details. To get an accurate configuration, each object or material is drawn carefully and classified under different layers. For instance, fourteen different layers in the considered environment scenarios were introduced: concrete walls, plywood walls and ceiling, doors, LCD projector, six lamp boxes, three speakers, one fire detector, duct, big heater, pillar, windows and their frames and boards in aluminum, as illustrated in Figure 2.2. More studies on the environment structure that affects signals propagation will be elaborated in the following chapter. Usually, advanced probabilistic or statistical algorithms are applied to match the online collected signals with the radio map, while deploying a defined number of reference tags. Thus, the computational complexity grows with the size of the database.

In our case, the training stage is performed following radial paths to limit the number In the following subsections, our conventional RFID ILS will be introduced. The autoradio map technique with the robot's calibration experiments are detailed in subsection 2.4.1.

The RSS averaging combining technique, followed by the One Slope propagtion Model (OSM)

and the multilateration, are described in subsections 2.4.2, 2.4.3 and 2.4.4, respectively.

Auto-Radio Map

In the aim of improving the calibration reliability as well as the localization accuracy, we investigate the robot's displacement issue. We start by an overview on the robot's systematic error and the way of calibration to typically keep it on the considered trajectory and collect the RSS acquisitions accurately, in both offline and online stages.

Most mobile robots induce systematic errors caused by imperfections in the design and mechanical implementation [21]. Therefore, robot's calibration is a key process to achieve proper results in the odometry-based navigation of any moving system.

Odometry is a fundamental robot's calibration technique. It is used in robotics to improve auto-fingerprinting and to typically keep the robot on track [22]; it handles data from motion to estimate changes in position over time; Moreover, well-calibrated odometry is an essential phase for a mobile robot to have an accurate displacement over a long path; this can be achieved through different test scenarios.

As robot platform, the model Pioneer 3-DX [23], shown in Figure 2.7, is used in the experiment. Pioneer 3-DX is a two-wheeled robot with dimensions 45.5 x 38.1 centimeters.

The Software Development Kit (SDK) provided by the brand is used to control it combined with the Advanced Robot Interface for Applications (ARIA), which is a C++ library for all mobile robot platforms, allowing to access all parameters such as speed and heading.

For navigation, the two key factors are: the robot's deviation and stop estimation [24].

To guarantee accurate displacement, many experiments have been carried out by a master student on the robot odometry errors such as: the straight line, the wheels' velocity, the wheels' rotation, and the square path calibrations tests. It can be noticed that the robot deviates to the left and hits the wall at a distance of 11.6 meters. This deviation can be neglected at the beginning. But, correction is required as the robot moves forward. Before evaluating the localization system performance, further investigations about the robot's displacement are needed to correct its deflection. A straight trajectory may be obtained by changing the speed of the left wheel.

Wheels' Velocity Test

In this subsection, the wheels' speed test was applied in order to figure out the origin of the robot's drift away from the straight line. ARIA has some functions that make it possible to obtain the linear speed of each wheel. Test was done over a straight line of 5 meters by displaying the speed of each wheel every second. Experiment was repeated three times.

Angular velocities (rad/s) are converted into linear speeds (mm/s) using the equations expressed below:

𝑉𝑉 𝑇𝑇 = 𝑅𝑅. 𝑊𝑊 𝑇𝑇 (2.1)
And

𝑉𝑉 𝑟𝑟 = 𝑅𝑅. 𝑊𝑊 𝑟𝑟 (2.2)
With 𝑉𝑉 𝑇𝑇 and 𝑉𝑉 𝑟𝑟 are the linear speed of the left and right wheel respectively. 𝑊𝑊 𝑇𝑇 and 𝑊𝑊 𝑟𝑟 are the angular velocity of the left and right wheels respectively. R is the wheels' radius.

Knowing that the wheels radius is of 92.5 mm [23], Table 2.4 represents the absolute difference between the speed of the right and left wheel. Based on these tests, the difference between speeds is quiet small with a worst case of 0.04 mm/s. In short, the problem of the robot's deviation was not due to the difference in the wheels' velocity.

Wheels' Rotation Test

As the wheels speed is not behind the robot's drift, wheels rotation test is necessary to analyze the wheels' rotation stability. Thus, the robot was rotated about itself 360° at the same speed in two directions i.e. ClockWise (CW) and CounterClockWise (CCW). The difference in angular velocity between the two wheels was analyzed. Then, the angle deviations are measured in both directions. Figure 2.9 presents the rotation errors for the six trials. whereas it rotates around 362° on average. It can be noticed that the wheels' rotation is almost stable and cannot be considered as the cause of the robot's deviation. Hence, another test is finally elaborated to show the robot's performance in a complete cycle path.

Square Path Test

For this test, the procedure defined as the University of Michigan Benchmark test (UMBmark) [25] is adopted as it is especially designed to uncover certain systematic errors.

This method is a set of test runs in which the robot is programmed to follow a 4×4 meters square path, as shown in Figure 2.10. Due to systematic errors, after linear and turning movement, the robot had a position offset and could not get back to the initial point.

Figure 2.10 Diagram of a square path clockwise and counterclockwise

For both CW and CCW scenarios followed by the mobile robot, offsets were studied upon ten trials. The average of each angle was calculated as shown in Table 2.5. Upon tests results', it can be concluded that the robot tends to drift to the left. However, the deviation angle is relatively small compared to 90°. Over short distances (less than 1 meter), and with the large size of the robot that is equal to 45.5 centimeters, the deviation does not significantly affect the robot's displacement accuracy. It corresponds to only 6.66% of the robot length.

Over a longer trajectory, and referring to the straight-line test (Figure 2.7), the robot presents an angle deviation of tan -1 (θ) = 0.8/11.6 = 3.95° to the left. Thus, to have an accurate mapping coverage and stable robot's motion, an auto-correction by a rotation of 3.95° clockwise needs to be applied.

RSSs Combining by Averaging

According to the literature, most common calibration databases use the averaged RSS value computed at a particular distance from the transmitting antenna [26]- [30]. In this framework, [28] puts forward an RSS based localization system using wireless sensor networks. Experiment is performed in a laboratory of 77 square meters. The obtained optimal positioning error is of 2 meters. [29] proposes a new synchronization protocol between the offline and online stages of the auto-fingerprinting based on averaging RSSs. The location accuracy of the proposed indoor localization system reaches 2 meters. In the same context, [30] also presents a system using the average of the RSS values as combining technique;

performance of the proposed architecture is analyzed to achieve a location accuracy of 1.22 meters. Thus, we adopt the average technique as RSS combining method in our conventional

Calibration and Distance Estimation by One Slope Propagation Model

To estimate the tag-reader distance from the received power, we need to apply a signal propagation model. Up to date, the One Slope propagation Model (OSM) is still the most commonly used in indoor localization applications [31] because of its simplicity.

OSM consists in a deterministic analysis that can only be applied in few rather simple cases.

In free space, according to Friis formula [33], the received power Pr_out by an antenna at a distance d from a transmitter can be represented by:

𝑃𝑃 𝑟𝑟_𝑃𝑃𝑜𝑜𝑇𝑇 (𝑑𝑑) = 𝑃𝑃 𝑇𝑇 . 𝐺𝐺 𝑇𝑇 . 𝐺𝐺 𝑟𝑟 . � 𝜆𝜆 4𝜋𝜋𝑑𝑑 � 2 (2.3)
Where 𝑃𝑃 𝑇𝑇 is the transmitted power in watt, Gt is the transmitting antenna gain, Gr is the receiving antenna gain, λ is the wavelength in meters, and d is the distance in meters.

In the logarithmic scale, the received power 𝑃𝑃 0_𝑃𝑃𝑜𝑜𝑇𝑇 at 1 meter in dBm can be expressed as follows:

𝑃𝑃 0_𝑃𝑃𝑜𝑜𝑇𝑇 (𝑑𝑑 = 1) = 10. log (𝑃𝑃 𝑇𝑇 . 𝐺𝐺 𝑇𝑇 . 𝐺𝐺 𝑟𝑟 . � 𝜆𝜆 4𝜋𝜋 � 2 ) (2.4)
And, the received power in outdoor, in dBm can be expressed as follows:

𝑃𝑃 𝑟𝑟_𝑃𝑃𝑜𝑜𝑇𝑇 (𝑑𝑑) = 𝑃𝑃 0_𝑃𝑃𝑜𝑜𝑇𝑇 -20. 𝑙𝑙𝑙𝑙𝑎𝑎 10 (𝑑𝑑) (2.5)
Where 𝑃𝑃 𝑟𝑟_𝑃𝑃𝑜𝑜𝑇𝑇 is the received power, 𝑃𝑃 0_𝑃𝑃𝑜𝑜𝑇𝑇 is the received power in the free space at distance 1 meter from the transmitter.

Indoors, the attenuation factor varies between 1.6 and 5 [32]. This variation is due to NLoS and multipath effects.

In our case, the conventional ILS applies the OSM defined as follows:

𝑃𝑃 𝑟𝑟 (𝑑𝑑) = 𝑃𝑃 0 -10. 𝑛𝑛. 𝑙𝑙𝑙𝑙𝑎𝑎 10 (𝑑𝑑) + 𝑋𝑋 (2.6)

With 𝑃𝑃 𝑟𝑟 (𝑑𝑑) is the received power in dBm, 𝑃𝑃 0 is the received power at 1 meter in dBm.

n defines the attenuation coefficient. The term 𝑋𝑋 describes the standard deviation of the received power values throughout the corresponding area.

localization accuracy compared to the trilateration approach, where only three fixed tags are needed, but position is determined based on three distances instead of N.

Expanding and regrouping terms in equation (2.7) we obtain:

A.� 𝑎𝑎 𝑏𝑏 𝑐𝑐 � = B (2.8) With 𝐴𝐴 = 2((𝑚𝑚 𝑇𝑇+1 -𝑚𝑚 𝑇𝑇 ) (𝑦𝑦 𝑇𝑇+1 -𝑦𝑦 𝑇𝑇 ) (𝑧𝑧 𝑇𝑇+1 -𝑧𝑧 𝑇𝑇 )) (2.9)
And

𝐵𝐵 = (𝑑𝑑 1 2 -𝑑𝑑 𝑇𝑇+1 2 -[(𝑚𝑚 1 2 -𝑚𝑚 𝑇𝑇+1 2 ) -(𝑦𝑦 1 2 -𝑦𝑦 𝑇𝑇+1 2 ) -(𝑧𝑧 1 2 -𝑧𝑧 𝑇𝑇+1 2 )]) (2.

10)

Where i is the index of the deployed RFID tags i.e, i ∈ {1, 2, …, (N-1)}.

The solution of equation (2.8), that corresponds to the intersection of the circles, determines the reader's coordinates as follows:

� 𝑎𝑎 𝑏𝑏 𝑐𝑐 � = 𝐴𝐴 -1 B (2.11)
In our system, we are working in a two-dimensional plane as four deployed RFID tags and the reader are at the same height. Given the asymmetrical shape of the classroom walls (Figure 2.1), we chose to reduce the effects of the ground and the ceiling reflections which are rather symmetric. In this spot, the RFID tags are fixed horizontally at the center of each wall, and at the same reader's height of 1.25 meters from the ground.

Thus, the applicate parameter can be omitted. For instance, P(a,b) represents the unknown reader's location. (𝑚𝑚 𝑇𝑇 ,𝑦𝑦 𝑇𝑇 ) represents the i th known tag coordinates, and d i represents the distance between the reader and the i th tag (Figure 2.13).

Figure 2.13 Positioning by Multilateration in Two-dimensional Layout

Only three RFID tags, fixed on three walls, could be used to locate the reader. We rather used four tags, fixed horizontally at the centers of the four walls respectively, in order to improve more the positioning accuracy. Hence, four quadratic equations are considered to estimate the RFID reader position given by:

� 𝑎𝑎 𝑏𝑏 � = 𝐴𝐴 -1 B (2.12) With 𝐴𝐴 = 2((𝑚𝑚 𝑇𝑇+1 -𝑚𝑚 1 ) (𝑦𝑦 𝑇𝑇+1 -𝑦𝑦 1 )) (2.13) And 𝐵𝐵 = (𝑑𝑑 1 2 -𝑑𝑑 𝑇𝑇+1 2 -[(𝑚𝑚 1 2 -𝑚𝑚 𝑇𝑇+1 2 ) -(𝑦𝑦 1 2 -𝑦𝑦 𝑇𝑇+1 2 )]) (2.14)
i is the index of the deployed RFID tags i.e, i ∈{1, 2, 3}.

RFID Equipment

The ILS used active UHF-RFID tags and a reader that operate at 433 MHz, as this frequency offers a larger communication range than that provided by the 2.4 GHz band used in most indoor localization systems and is less affected by multipath fading [36].

The RFID equipment is proprietary of Ela-Innovation; it does not comply with RFID standards. Figure 2.14 (a) shows the "Coin ID" tag. This UHF RFID tag can be detected from as far as 20 meters indoors. It presents a fast identification time (less than 1 second). Moreover, it operates according to an active identification process in periodic transmission. More precisely, transmission is non-continuous, the signal emission time is around 1 millisecond and the time difference between two consecutives transmissions is around 200 milliseconds. In the same context, this active RFID tag is powered by a 3 Volt battery. Its antenna has an omnidirectional radiation pattern [37].

The RFID reader is illustrated in Figure 2.14 (b). It is an "UTP Diff 2", powered by a 6

V battery and can be configured using onboard instructions as well as a software interface, that does not include any anti-collision protocol. The conventional system is our baseline; it will be improved in the following chapters in the aim to feature cost effective RFID tags deployment, along with low RSS variability, as well as enhanced radio map reliability and location accuracy. This chapter focuses on presenting the accurate environment model. Tools used to design the 3D layouts will be introduced in section 3.2. In section 3.3, two indoor environments are well presented with all modeling details. In section 3.4, the different steps for simulation, including the simulated RFID tag's antenna, reader antenna and configured indoor environments, will be expanded. In section 3.5, a comparison between the power values collected by the reader through simulations and real measurements is dressed. Finally, the summary of this chapter is presented in section 3.6.

WinProp tool

There are several commercial software that predict signals propagation indoors such as EDX [1], Ranplan [2] and WinProp [3]. Most of these tools provide fast simulations, thanks to the preprocessing of the indoor materials' properties that is required for the prediction [4].

WinProp simulator has been selected to model the indoor environment and the RFID hardware based on its reasonable license cost and its features to import CAD files for electromagnetic simulations, as well as it provides accurate and fast propagation models in addition to the empirical and semi-deterministic methods for radio coverage and network planning. Moreover, [5] presents a satisfactory realistic results showing good agreement between simulations and measurements. These propagation models differ depending on the prediction accuracy and the computational resources. Performance of this software is reported in the literature as follows:

[5] characterizes signals behavior over the 700 MHz band with different propagation models.

According to the presented results, Ray-Tracing provides more accurate modeling than that by other methods. [6] verifies WinProp capability to correctly predict the propagation characteristics of the 5G radio coverage at millimeter-wave bands in different urban city centers. In this context, [7] compares empirical or Ray-Tracing propagation models to real measurements. It shows that the new approach, based on 3D vector building databases, exceeds the accuracy of the Dominant path propagation model.

Environments for Tests

We aim to characterize signals behavior in two considered indoor environments, using active RFID tags operating at 433MHz. The RFID equipment used is already presented in chapter 2, section 2.6.

Experiments are carried out in the following environments: an empty classroom on the fourth floor and a hall on the ground floor of the Engineering School EFREI-Paris.

It is worth recalling that the classroom has typical dimensions, with an area of approximately 63.75 square meters (8.5 x 7.5 square meters). Although unfurnished, this environment may still be considered as complex for RF signals propagation due to its asymmetrical walls. It also contains some unmovable metallic objects such as the heater (with 8 meters of length and 1 meter of height) with sharp blades, LCD projector, fire detector, and speakers (Figure 3.1).

Environment Properties

The use of AutoCAD is helpful to design the indoor environment with small details.

To get an accurate configuration, each object or material is drawn carefully and classified under different layers.

For instance, fourteen different layers in the considered classroom were introduced: concrete walls, plywood walls and ceiling, doors, LCD projector, six lamp boxes, three speakers, one fire detector, duct, big heater, pillar, windows and their frames and boards in aluminum, as illustrated in Figure 3.3.

Figure 3.3 3D Layout of the classroom environment

Furthermore, eight different layers in the considered hall environment were introduced: concrete walls, plywood walls and ceiling, doors, twelves lamp boxes, two fire detector, big heaters, windows and their frames, as illustrated in Figure 3.4.

Figure 3.4 3D Layout of the hall environment

Through WallMan, the layers' database is converted from the Cad file to generate a database for each environment; each layer, already defined through AutoCAD, corresponds to a specific material.

Figure 3.5 presents the flowchart to design the 3D simulated environment via WallMan.

Figure 3.5 Flowchart for the generation of vector databases

While generating the environment database, empirical losses and Fresnel parameters that correspond to each material are defined depending on the material thickness and the frequency used. As already mentioned, up to fourteen different layers constitute the considered indoor environments. Table 3.1 exposes the Fresnel parameters of the main present materials.

Tag's Antenna Model

As it is well known, signals propagation relies on the antenna's radiation pattern. To predict the signal coverage, the pattern must be described accurately within the radio network planning tool.

Through AMan, which is a graphical antenna pattern editor, the tag's antenna is HPI is the most common and accurate algorithm used for centered and non-centered radiation pattern beams. This algorithm considers any shape of antenna and tracks the maximum gain direction. Any beam tilt will be better handled by the HPI.

For the conversion patterns settings, we selected the HPI algorithm 2x2D to 3D, through

AMan, that will be used for the RF propagation model. Hence, the 2D vertical and horizontal tag antenna patterns are combined to generate the 3D pattern under the extension *.apb, as presented in Figure 3.9. The red color represents the 3D radiation pattern of the tag's antenna and the gray color is the grid envelop of this pattern.

Figure 3.9 3D radiation pattern of the tag antenna with AMan

Reader's Antenna Model

Feko is an electromagnetic solver combining several numerical methods in the frequency and time domains. Direct and indirect hybridization between numerical methods are also available to solve electrically big structures. Feko offers a panel of numerical methods/solvers included in one interface (Figure 3.10). Depending on the complexity and the electrical structure size, one can adapt a dedicated numerical method. Simulation of complex and electrically large scenarios can also be solved by using full hybridization between numerical algorithms. Feko is mainly based on the Method of Moments (MoM), which is a numerical computational method of solving linear partial differential equations that have been formulated as integral equations i.e. in the boundary integral form [14].

In our case, the MoM is used to model the RFID reader. It is based on the full-wave method without any convergence algorithm; this makes it accurate as far as the meshing is well defined.

Figure 3.10 Feko numerical methods overview [15]

Based on the Ela Innovation RFID reader datasheet [16], reverse engineering has been achieved to design the reader antenna model. Using Feko, the antenna is configured and the characteristics of the reader are used as inputs to the simulator.

The RFID reader antenna consists in two monopoles mounted on the ground plane and excited in the spatial diversity mode: when one monopole is receiving, the other one is loaded and vice versa. The RFID reader box is made from plastic materials with the following characteristics: Relative Dielectric Permittivity 𝜀𝜀 𝑟𝑟 = 4 and losses tan δ = 0.02.

The reader meshing is based on equilateral triangles to represent the geometrical cad and assign a basis function to each triangle node (Figure 3.11). This meshing type is the default one used in Feko for MoM with mesh size of 𝜆𝜆/16, which is related to this solver algorithm to have accurate current sampling.

Currents are then calculated on the node of each triangle using the MoM [14]; the sum of these currents will contribute to the calculation of the radiated field [15]. This sum is based on three types of currents:

-Linear currents on the antenna segments -Electric current on the metallic triangles, e.g. currents on the antenna's ground plane -Magnetic current in the dielectric triangles, e.g. currents on the plastic box.

A locally refined mesh has been applied around the RFID reader antennas where the antenna source or feeding is located as currents are highly varying in this zone (Figure 3.11).

Figure 3.11 Meshed structure for the RFID reader box

The radiation pattern of the RFID reader antenna has a dissymmetric shape due to the proximity of the loaded antenna. When one antenna is excited, the other antenna is 50 Ω loaded and vice versa. Since the loaded antenna is not radiating, it will behave as a metallic object beside the radiating antenna. This leads to an unsymmetrical radiation pattern shape. This pattern will be exported in *.ffe format (ASCII format) compatible with WinProp and will be used as a receiving antenna (called Mobile Station in WinProp).

As shown in Figure 3.12 and Figure 3.13, the antennas' pattern is not similar to the omnidirectional one. It is designed based on ELA Innovation reader datasheet [16].

Propagation Manager (ProMan)

The main tool in WinProp is ProMan (Propagation Manager). ProMan is where the simulation project settings are defined and edited, and where results are illustrated.

In ProMan, different propagation models exist in order to simulate signals propagation in an indoor environment; for instance, the Modified Free Space Model (MFSM) or the One Slope propagation Model (OSM) already presented in the chapter 2, subsection 2.3.3. This is the most common propagation model; a simple linear formula determines the path loss.

However, the position and dimensions of walls, obstacles and material's properties are relatively ignored as a prior database. The Multi-Wall Multi-Floor (MWMF) model includes only losses introduced by walls and floors; it is applicable for an entire floor or building.

Other propagation models need to be used to predict the signal coverage indoors, while not only considering all losses (already defined through WallMan) but also reflections, diffractions, and scattering. The most accurate existing propagation models for indoor scenarios founded in ProMan, are the Dominant Path (DPM) and the Ray-Optical Models.

The Dominant Path Model

Indoors, the signal propagates through different paths to reach the receiver.

Ray-Optical Models

There are two different approaches to determine the Ray-Optical propagation paths between transmitter and receiver: Ray-Tracing and Ray-Launching [18].

The Ray-Tracing model computes the power at each receiving point with a constant resolution and uses the image principle to determine the reflection point (Figure 3.17).

Whereas, the Ray-Launching performs a constant angle increment for the ray shooting.

Depending on the Ray-Launching angle of increment, some details can be neglected or in other word not illuminated by the rays. This fact can lead to a lower accuracy. Figure 3.17 illustrates the difference between the Ray-Tracing and the Ray-Launching models. -

Standard Ray Tracing (SRT)

The Standard Ray-Tracing model performs a rigorous 3D Ray-Tracing prediction; it results in very high accuracy because it takes into consideration the effect of the environment on the propagation parameters. This model requires a large computational time.

-Intelligent Ray Tracing (IRT)

The Intelligent Ray-Tracing model is used to accelerate the classical ray optical models such as SRT. This model is based on a preprocessing of the environment properties. All walls of the indoor building are divided into tiles, and all wedges are subdivided into horizontal and vertical segments.

Summary for the different Propagation Models

Table 3.3 presents the different indoor propagation models available in WinProp with their characteristics. Hence, if there is any modification in that database, it must be modified through the original one. And, a new preprocessing is requested. Flexibility and better control of the database were required in our research. This pushed us to use the SRT as the best trade-off between accuracy and flexibility.

Principle of the Ray-Tracing Model

Within the Optical-Ray Tracing model, all rays are followed until they hit an obstacle.

Afterwards, reflection or transmission or both will occur, depending on the obstacle materials [20]. The direction of the new ray is determined by Snell's law [21]. Losses due to rays reflections' depend on the thickness and the material characteristics of the hit obstacle at the respective frequency [22]. The relationship between the optimum thickness of the material and the angles of incidence is expressed by:

𝑑𝑑 = 𝑝𝑝. 𝑐𝑐 2�𝜀𝜀 𝑟𝑟 -𝑠𝑠𝑖𝑖𝑛𝑛 2 𝜃𝜃 -𝑠𝑠𝑖𝑖𝑛𝑛 2 𝛼𝛼 - 𝑠𝑠𝑖𝑖𝑛𝑛 2 𝜃𝜃 𝑠𝑠𝑖𝑖𝑛𝑛 2 𝛼𝛼 𝜀𝜀 𝑟𝑟 (3.1)
d is the thickness of the material; 𝜃𝜃 and α are the angles of incidence in azimuth and elevation planes; p is an integer constant for each optimal thickness; c is the light celerity in free space and 𝜀𝜀 𝑟𝑟 is the relative dielectric permittivity of the material.

Diffracted rays can be considered when applying the Uniform Theory of Diffraction method (UTD) [9]- [11]. In addition to the reflection and transmission behavior, losses inside the materials must be considered for better accuracy.

To better understand the signals propagation behavior, a review of the reflection and transmission coefficient, in terms of the incident angle and various obstacles within the environment, is useful. Hence, it is necessary to characterize electrically the various materials existing in the environment. For instance, a plane electromagnetic wave, crossing to the planar interface between two regular semi-infinite areas 1 and 2, gives rise to two waves: reflected and transmitted (or refracted). According to Snell's law, applied to the EM propagation through the dielectric slab [23], the reflection and transmission coefficient for the Transverse Electric (TE) mode are calculated as follow:

𝑅𝑅 𝑇𝑇𝑀𝑀 = 𝜇𝜇 2 2 √
With 𝜃𝜃 𝑇𝑇 is the angle of incidence; 𝜀𝜀 𝑇𝑇 and 𝜇𝜇 𝑇𝑇 are the dielectric permittivity and the magnetic permeability of the material. It is worth mentioning that indices 1 and 2 correspond to the two regular semi-infinite areas.

Recall that, to get accurate simulations, the environment properties, the empirical transmission and reflection coefficients should be well defined in WallMan. Through ProMan, the computation of the signal propagation takes into consideration all these parameters.

Indoor Radio Coverage

In this subsection, the model of the classroom and the hall, where the proposed system and approaches will be tested, is analyzed. As previously stated in section 3.4.1, the behavior of the radio channel in indoor scenarios heavily depends on the complexity of the environment.

The defined materials parameters' in WallMan and the creation of the tag's antenna via AMan are imported to ProMan, in order to study the signal propagation in the designed environment. 3.4 shows the measured and simulated (tag-reader) distance errors over the track A90. and NLoS between the transmitting tag and the reader (for tags 4 and 5). In this context, the differences between the real and the simulated received power, that exceed 0.5 dB at some positions over the two tracks, are mainly due to the NLoS and the neglection of some small details that couldn't be took in consideration. Despite the high accuracy of 3D Standard Ray-Tracing, this model is still limited for large environments like our hall, where the prediction positions are far away from the transmitting antennas.

Summary

The chapter presented the models of two indoor environments as well as those of the RFID equipment that will be used in our solution. Numerical methods such as the HPI However, those over tracks A120, A135 and A150 spread and appear to span a wider range. RSS values over track A120 vary between 138 and 142 corresponding to -56.32 and -58.84 dBm. In the same context, the RSS acquisitions over tracks A135 and A150 are spread between 134 and 138 which correspond to -53.79 and -56.32 dBm, respectively. These wide distributions over tracks A120, A135 and A150 are mainly due to the signals reflection and scattering created by the big heater (Figure 4.1). Therefore, accrediting only averaging as RSS combining technique will lose the high-order statistical information in the raw data. Thus, the location system accuracy would be compromised. To improve the system's reliability, the use of the MLE in order to determine the RSS value with the highest probability of occurrence is promising.

RSS Datasets

RSS Combining Techniques

RSS Combining by the Conventional Averaging

As already elaborated in chapter 2, subsection 2.

RSS Combining by the MLE

The maximum likelihood of a variable is the value having the highest probability of occurrence, while observing a given set of that variable [10]. MLE is used for different applications [11], [12]. For instance, [11] adopts the MLE in artificial intelligence to understand the language, by predicting the most consistent sequence with the highest probability of occurrence. Considering one sequence predictions of the target sequence y with parameters 𝜃𝜃, MLE aims to train the sequence prediction models by minimizing the negative log-likelihood of the probability 𝑝𝑝 𝜃𝜃 (𝑦𝑦|𝑚𝑚) as follows:

𝐿𝐿 𝑀𝑀𝑀𝑀𝑀𝑀 (𝜃𝜃) = -𝑙𝑙𝑙𝑙𝑎𝑎 𝑝𝑝 𝜃𝜃 (𝑦𝑦|𝑚𝑚) (4.2)
With y = {𝑦𝑦 1 , 𝑦𝑦 2 , … , 𝑦𝑦 𝑇𝑇 } is the sequences set and x is the source type such as phrase, sentence or passage of human language or even an image.

In [12], the MLE is used to estimate and remove the frequency offsets at the ground station, in order to get successful communications with a satellite. Given a block of N samples of the received noisy signal, the MLE produces the frequency value that maximizes the periodogram, as follows:

𝑓𝑓 ̂𝑀𝑀𝑀𝑀𝑀𝑀 = 𝑎𝑎𝑎𝑎𝑎𝑎𝑚𝑚𝑎𝑎𝑚𝑚 𝜆𝜆 𝐼𝐼(𝜆𝜆) (4.3)
Where 𝐼𝐼(𝜆𝜆) is the plot of the periodogram function for a noiseless signal, 𝜆𝜆 is the wavelength.

In our case, to improve the calibration phase's reliability and the localization accuracy, the effect of the RSS values deviation must be mitigated through the associated combining technique providing the most reliable value of RSS in both offline and online stages. Hence, for further processing, the RSS samples are combined with the Maximum Likelihood Estimator (MLE) which is a statistical approach that computes the distribution probability factor of the received power values [13].

Given K acquisitions at each position, the MLE determines the RSS value that maximizes the likelihood with the real power captured by the RFID reader; this RSS value is that with the highest probability of occurrence.

𝑅𝑅𝑅𝑅𝑅𝑅 = [𝑅𝑅𝑅𝑅𝑅𝑅 1 , 𝑅𝑅𝑅𝑅𝑅𝑅 2 , … . . 𝑅𝑅𝑅𝑅𝑅𝑅 𝐾𝐾 ] (4.4)
With K is the size of RSS acquisitions at the corresponding position.

The MLE of RSSs is defined as:

𝑅𝑅𝑅𝑅𝑅𝑅 𝑀𝑀𝑀𝑀𝑀𝑀 = 𝑎𝑎𝑎𝑎𝑎𝑎 𝑚𝑚𝑎𝑎𝑚𝑚 𝑝𝑝(𝑅𝑅𝑅𝑅𝑅𝑅 𝐾𝐾 ) (4.5)
Where argmax is the operator that gives the RSS with the greatest probability of occurrence. p represents the RSS probability.

ITU-R 2135 Model

Based on real measurements carried out in China, WINNER II model was modified and validated by the ITU-R M.2135 [8]. ITU-R are specified in the frequency range from 2 GHz to 6 GHz. Four different scenarios have been studied: indoor hotspot, urban, suburban, and rural.

In the indoor hotspot scenario, it is adopted for a floor of 6 meters of height, including a big Where 𝑃𝑃𝐿𝐿(𝑑𝑑)in dB, f is the frequency in GHz, d is the distance between the transmitter and receiver in meters.

The Multi Wall Multi Floor Model

The Multi Wall Multi Floor (MWMF) model was validated for two different Where 𝑃𝑃𝐿𝐿 𝑀𝑀𝑊𝑊𝐹𝐹 (𝑑𝑑) is the path loss in dB at a distance d in meters. 𝐿𝐿 𝑤𝑤𝑇𝑇𝑤𝑤 is the loss of the 𝑘𝑘 𝑇𝑇ℎ crossed wall of type i in dB, 𝐾𝐾 𝑤𝑤𝑇𝑇 is the number of crossings through walls of type i, I is the number of walls' type i, 𝐿𝐿 𝑓𝑓𝑓𝑓𝑤𝑤 is the loss of the 𝑘𝑘 𝑇𝑇ℎ penetrated floor of type j in dB, 𝐾𝐾 𝑓𝑓𝑓𝑓 is the number of crossings through walls of type j and J is the number of floors' type j.

than 3𝜆𝜆 and another one for distances greater than 3𝜆𝜆, which is equal to 2.

Dual One Slope with Second Order Polynomial Model

As previously mentioned, measured power values are separated into two slopes, since the received power highly decrease for tag-reader distances shorter than 3𝜆𝜆 (which is equal to 2.1 meters) and vary slightly for distances greater than 3𝜆𝜆. A detailed analysis is described and illustrated in section 4.5. The weighted average coefficients 𝐴𝐴 𝑤𝑤2 , 𝐵𝐵 𝑤𝑤2 and 𝐶𝐶 𝑤𝑤2 , corresponding to the second order polynomial modeling the second segment of the propagation channel, are then defined as: the respective number of positions over each track.

Propagation Models Representations

⎩ ⎪ ⎪ ⎪ ⎨ ⎪ ⎪ ⎪ ⎧ 𝐴𝐴 𝑤𝑤2 = � a T i 7 i=1 N′′ Pos T i � N′′ Total 𝐵𝐵 𝑤𝑤2 = � b T i 7 i=1 N′′ Pos T i � N′′ Total C w2 = � c T i 7 i=1
It can be noticed that WAAF coefficients are directly proportional to the number of calibrated positions. This procedure takes into account all positions in order to cover the whole environment.

To evaluate the accuracy improvement thanks to WAAF calibration, a comparison between Estimated Distance Errors obtained, by applying the conventional RSS average combining technique, with AF90 and WAAF attenuation factors is elaborated.

It is worth recalling that, within the online stage, RSSs are collected every 70 centimeters ≅ 𝜆𝜆, over the three tracks A60, A90 and A120, selected for the validation of the proposed calibration procedure improvements'.

Hence, examined estimated distance errors at two tag-reader distances of 2.8 ≅ 4𝜆𝜆 and 3.5 ≅ 5𝜆𝜆 meters, are illustrated in Table 4.2. These are the nearest distances to 3 meters; value where the RSS fluctuations were studied in section 4.2. Finally, once the RSS combining approach by the MLE is introduced and the WAAF calibration technique is elaborated, the following section is dedicated to present the overall proposed ILS.

Complete Proposed ILS System

In short, the proposed ILS based on MLE with WAAF is divided into two stages: offline and online stage. In short, the DOSSOM with WAAF calibration improves the position accuracy of 35 percent, as compared to DOSSOM.

Overall, the suggested indoor localization architecture improves the position accuracy by up to 41 percent hence reducing the position error from 2.2 to 1.3 meters.

Location Assessment by Averaging versus MLE

In this sub-section, experiments were conducted, using the calibration parameters AF90

and WAAF independently within the offline stage, in order to investigate the localization performance via averaging and MLE as RSS combining techniques.

As previously mentioned in section 4.3, these techniques are used to combine the received power acquisitions in both offline and online stages.

Obtained CDF, presented in the effect of multipath fading [5], [6]. In addition, the classical approaches consist of using multiple receiving antennas and performing combining or selection and switching techniques to improve the quality of the received signals. Based on this idea, the concept of using multiple antennas for localization is very challenging. Therefore, our proposed approach is focused on using a group of tags that operate together at the same frequency in our reader-based localization system. We named this configuration: a constellation of tags.

Moreover, several techniques can be used to exploit the antennas signals' diversity [7], [8]. In our work, the MLE is suggested to combine the received signals strength and assess the location accuracy.

This chapter is organized into four sections; section 5.2 presents the constellation approach with different radii, shapes, and number of RFID tags. In section 5.3, the error of distances estimated with the optimal constellation are compared to those obtained with a single tag in two scenarios: real and simulated environments. Then, localization by the proposed system is assessed and analyzed with both the optimal constellation and the single tag configurations, and through the two different RSS combining techniques i.e. averaging and the Maximum Likelihood Estimator, in section 5.4. Finally, the last section focuses on conclusions based on obtained results.

Constellation of UHF-RFID Tags

We introduce the concept of constellation of RFID tags in indoor localization, aiming to minimize the location uncertainty. As already mentioned in the introduction, the constellation is a set of tags that are close to each other and operating at the same frequency.

Preliminarily, a MISO-based ILS is introduced in this section. Then, the constellation approach is proposed along with its different radii, shapes, and number of tags. By applying the One Slope propagation Model (OSM), mean estimated distance errors of the different constellations are analyzed and compared to each other to reach the optimal constellation of RFID tags.

MISO-Based Indoor Localization System

The MISO architecture is one of the several forms of smart antenna technologies with only one receiving antenna. Performance of wireless communication systems can be improved by adopting multiple antennas at the transmitting side [3]. Few papers present works using MISO for localization purposes [9]. Most traditional researches are focused on the channel capacity [10]. Accuracy errors, between the estimated and the actual trajectories, were 11.4 and 14.1 centimeters respectively. Despite good performance, [11] implemented the WSN in a homogenous indoor environment and [12] validated their approach without any interference and by simulations only. In the following, we present a technique to improve the accuracy of our low-cost RFID localization system in a classroom environment. The complexity of this classroom is attributed to its geometry and to the materials that constitute this environment, as previously discussed in chapter 3, sub-section 3.4.1.

Constellation of Active UHF-RFID Tags for Reader Localization

As previously mentioned in chapter 2, section 2.4, the RFID reader-based localization system includes tags used as anchor or beacons and a reader to locate. The suggested constellation of tags can have different shapes and may include a variable number of tags. For instance, Figure 5.2 presents the constellation of four active RFID tags for reader localization. First, our work was focused on investigating the optimal constellation's radius and shape as well as on the optimal number of tags constituting it. As multiple transmitting antennas can reduce multipath by benefiting from signals' diversity, different experiments with the constellation of RFID tags will be analyzed, in the following subsections, in order to increase the location accuracy.

Figure 5.2 Constellation of four RFID tags

Whereas, constellations with radius 5𝜆𝜆/4, 3𝜆𝜆/2 and 7𝜆𝜆/4, respectively, were evaluated through different simulations due to some practical aspects that prevent real measurements. Looking on values of Table 5.1, it can be noticed that the mean estimated distance error, obtained over track A90, presents a maximum error for the radius equal to 𝜆𝜆/8, which confirms the effect of coupling between tags' antennas. The minimal distance error is reached for the constellation with radius R equal to 𝜆𝜆. Moreover, the standard deviation presents low variation with the radius equal to 𝜆𝜆. Thus, a constellation of tags with radius 𝜆𝜆 will be used in the following tests.

Optimal Shape and Number of Tags

In order to determine the best constellation and after the selection of the optimal radius, it is also challenging to determine the best shape and the optimal number of tags that constitute the constellation. Several shapes, with a radius R equal to 𝜆𝜆, are studied over the same track A90: a triangle constellation with three tags ( It is worth mentioning that by increasing the number of RFID tags constituting the constellation, with a defined radius, the inter-tags distance "C" decreases and the coupling between tags antennas' increases (Figure 5.5).

In Table 5.2, it is apparent that there are fewer errors in the constellation with five tags where one of them is at the constellation center (Figure 5.5 (c)). However, adding an extra tag to the four-tag constellation, in order to improve the accuracy by just 2 cm, makes the choice of a four-tag constellation (diamond shape) cost effective.

Constellation versus Single Tag

In this section, performance of the optimal constellation of tags (diamond shape with a radius equal to λ) is compared to those obtained with a single tag under the same conditions.

To validate the constellation approach while avoiding the long durations needed for onsite measurements, a reliable modelling of the classroom environment can be a suitable alternative, as shown in Figure 5.6 . Received power values at the various positions were measured to characterize the signal behavior in terms of the tag-reader distance over the tracks A60, A90 and A120 (Figure 5.3).

Over each track, the estimated distances errors were analyzed first while a single tag is used, then with the optimal constellation of tags, and finally for each tag of the optimal constellation independently to see the behavior of each one. Actually, it is important to present

Measurements Accuracy

Based on real power measurements, estimated distances errors for the single tag and the constellation of tags scenarios are evaluated. Table 5.3 shows MDEs based on real power values collected by the RFID reader. Based on the MDEs presented in Table 5.3, it can be noticed that the constellation of four tags architecture presents less errors compared to that with a single tag. For instance, the constellation of tags reduces distances errors by 17 centimeters over the track A60, 42 centimeters over A90 and 45 centimeters over A120.

Moreover, the constellation of tags also presents more stability as the standard deviation is smaller than that for the single tag configuration. Thus, the concept of using multiple transmitting antennas, to reduce the effect of multipath fading on distances estimation, is well validated. Table 5.4 illustrates the MDEs with the single tag and with the constellation of tags configurations.

Localization Assessment based on Constellations and RSS Combined by MLE

As already demonstrated in chapter 4, the proposed ILS presents an improvement in the location accuracy while using the MLE as RSSs combining technique in the single tag scenario.

Assessment presented in 4.8.2, multilateration along with DOSM, and DOSSOM with WAAF indoor propagation models, are applied to estimate positions errors at the thirty-two different positions appearing in Figure 5.12.

Obtained results by performing the constellation approach are analyzed and compared to those results obtained in the single tag scenario. implementation simplicity and COCKTAIL's efficiency, the achieved high precision, using both algorithms, refers to the dense deployment of RFID tags i.e., around one tag per square meter.

In this framework, the different indoor localization architectures were studied and compared to the proposed system based on either single tags or constellation of tags, to evaluate its performance. The following Table C. 1 characterizes the different active UHF RFID-based ILSs found in the literature and ours, in terms of locations accuracy and the number of RFID tags deployed in the considered indoor environment. For this reason, we have suggested reducing the number of tags and improving the stability of the RSS via MLE. The proposed localization system based on either single tags or constellations is able to determine the reader's location with an optimal position error of 90

  by l'Ecole d'Ingénieurs Généraliste du Numérique -EFREI Paris.This study would not have been possible without the guide, supervision and support of several people who believed in me and encourage me to conduct this work and reach the end of this thesis.

  First, I would like to address a special acknowledgment to Prof. Fouzia Boukour and Prof. Yvan Duroc for accepting to evaluate and report my research work. I would like to thank Dr. Antoine Diet and Dr. Youmni Ziadeh for accepting to examine my manuscript. Foremost, I would like to thank Prof. Jean-Marc Laheurte and Prof. Bachar El Hassan for their support. My sincere gratitude goes to my sisters' advisors Dr. Sara Abou Chakra and Dr.

  Il existe plusieurs métriques et techniques appliquées pour la localisation et le suivi des personnes et des objets à l'intérieur des bâtiments. Elles doivent tenir compte de la complexité de l'environnement afin d'atteindre un niveau de précision acceptable pour l'application. Parmi ces métriques, le temps d'arrivée (Time of Arrival, ToA), la différence du temps d'arrivée (Time Difference of Arrival, TDoA), l'angle d'arrivée (Angle of Arrival, AoA) et la puissance du signal reçu (Received Signal Strength, RSS) sont les plus fréquentes. ToA et TDoA nécessitent une synchronisation. La mesure de AoA nécessite des dispositifs complexes et un calibrage précis afin d'estimer la position de la cible. La phase (Phase of Arrival PoA) et la différence de phase (Phase Difference of Arrival PDoA) sont deux métriques précises, mais moins utilisées.

  ZigBee est une technologie de communication sans fil. Elle présente un faible coût et une consommation d'énergie réduite. La portée du signal peut atteindre 100 mètres en espace libre. Alors que dans les environnements indoor, la couverture est généralement comprise entre 20 et 30 mètres. Comme Zigbee, Bluetooth est un système de communication personnel (Wireless Personal Area Network, WPAN). Il partage les informations avec un haut niveau de sécurité, un coût et une consommation d'énergie réduits. La portée de communication en indoor est comprise entre 10 centimètres et 15 mètres, suivant la visibilité directe ou non. Bluetooth est intégré dans la plupart des téléphones portables, montres intelligentes et ordinateurs portables. Cette technologie peut ainsi être utilisée dans les systèmes de localisation. La dernière version de Bluetooth (Bluetooth Low Energie, BLE) présente une amélioration de la plage de couverture pour atteindre 100 mètres en outdoor. Elle permet d'utiliser différentes métriques de positionnement telles que RSS, ToA, TDoA et AoA. Wireless Fidelity (WiFi) offre un faible coût et une faible consommation d'énergie. Récemment, la plage de couverture du WiFi (version 802.11ax) a été augmentée de 100 mètres à 1 kilomètre en indoor, notamment dans les applications IoT (Internet of Things). Vu que le réseau WiFi est couramment déployé dans les bâtiments, il peut également être utilisé dans des applications de localisation, en utilisant différentes métriques de positionnement. Ultra-Wide Band (UWB) est une technologie radio pour les communications à courte portée (moins de 100 mètres) et large bande passante. Cette technologie fonctionne dans la gamme de fréquence comprise entre 3.1 et 10.6 GHz, avec une faible consommation d'énergie. En localisation indoor, UWB présente une forte résistance contre les multi-trajets due à sa large bande passante. La position de la cible peut être estimée en utilisant ToA, TDoA et AoA. Ainsi, le système de localisation avec UWB a besoin de synchronisation entre les émetteurs et les xi récepteurs. Ce système de localisation présente également un coût élevé dû au besoin d'équipements spécifiques pour l'utilisateur. Les technologies d'identification par radiofréquence (Radio Frequency IDentification, RFID) sont devenues très courantes, notamment en logistique grâce à leur capacité d'identification en visibilité indirecte (NLoS) et leur faible coût. Les technologies RFID peuvent opérer dans plusieurs bandes de fréquence (Basse Fréquence de 125 kHz -134 kHz, Haute Fréquence à 13,56 MHz, Ultra Haute Fréquence à 433 MHz et de 850 à 960 MHz et Super Haute Fréquence à 2.45 GHz et à 5.8 GHz). La position de la cible peut être estimée en utilisant RSS, ToA, TDoA et AoA. La précision de la localisation augmente avec le nombre de balises/tags RFID déployées dans l'environnement. Ces balises peuvent être classées en actives et passives. La balise active dispose d'un émetteur et est alimentée par batterie, ce qui la rend plus lourde et coûteuse. Elle permet une plage de couverture de 200 mètres. La balise passive fonctionne sans émetteur et avec ou sans batterie, elle est beaucoup moins chère et plus petite que la balise active. Le lecteur RFID est responsable de l'alimentation et la communication avec la balise passive. L'antenne de la balise capte l'énergie et transmet son identifiant. Sa plage de couverture est très variable selon la bande de fréquence utilisée, typiquement de quelques dizaines de centimètres à plusieurs centaines de mètres dans certains cas. La distance est de 1 à 3 mètres en moyenne pour les systèmes fonctionnant à 860 MHz.

(

  Figure R.1 Objectifs et contributions

  Figure R.4 Outils de simulation

  Considérant K acquisitions à chaque position, le MLE détermine la valeur RSS qui maximise la vraisemblance avec la puissance réelle mesurée par le lecteur RFID; cette valeur est celle avec la probabilité d'occurrence la plus élevée. 𝑅𝑅𝑅𝑅𝑅𝑅 = [𝑅𝑅𝑅𝑅𝑅𝑅 1 , 𝑅𝑅𝑅𝑅𝑅𝑅 2 , … . . 𝑅𝑅𝑅𝑅𝑅𝑅 𝐾𝐾 ] (R.1) xviii Avec K est le nombre d'acquisitions par position.

  facteur d'atténuation global pour l'environnement via la moyenne pondérée des facteurs d'atténuation (WAAF). WAAF correspond aux facteurs d'atténuation de la salle de classe en prenant en couvrant tout l'environnement. Dans ce contexte, les coefficients d'atténuation 𝑛𝑛 𝑇𝑇 𝑖𝑖 , 𝑎𝑎 𝑇𝑇 𝑖𝑖 , 𝑏𝑏 𝑇𝑇 𝑖𝑖 𝑒𝑒𝑒𝑒 𝑐𝑐 𝑇𝑇 𝑖𝑖 correspondants au modèle DOSSOM de l'équation (R.4) et associés aux sept trajets A30 à A150 (Figure R.3 a), sont pondérés afin de déterminer les coefficients WAAF définis par les équations suivantes : Pour d ≤ 3

7 ) 8 .

 78 Figure R.6 CDF des erreurs de position avec la moyenne et MLE

Figure R. 8

 8 Figure R.8 CDF des erreurs de position avec constellations ou balise unitaireLes erreurs de position obtenues à 90% CDF sont de 90 et 60 centimètres avec une seule balise et la constellation de balises, respectivement. Ainsi, l'utilisation de la constellation de balises améliore la précision de localisation de 33.3%, tout en déployant, seulement, 0,25 balises RFID par mètre carré.

Frequency

  (RF). RF standards such as Wireless Fidelity (WiFi), Zigbee, Bluetooth, Ultra-Wide Band (UWB), and Radio Frequency IDentification (RFID) predominate today to develop accurate ILS. Despite the RF Non-Line-of-Sight (NLoS) detection capabilities, these can have an adverse impact on the overall ILS accuracy.Recently, RFID promoted the potential of the RF technology for indoor localization in many scenes. Multiple RFID readers and tags are used to form the RFID based localization system. The placement and density of RFID readers, tags or both, in a given layout, are key parameters to provide satisfactory localization accuracy. Estimated locations are usually more accurate with a higher density of components. However, this will increase the system's cost substantially. This Ph.D. thesis seeks to present a new ILS based on the RFID technology. The proposed system's must be reliable, accurate and characterized by an effective low cost. Hence, three main objectives are defined: improve the calibration, optimize the tags density, and enhance the location accuracy (Figure I.1) To this end, five main research lines are conducted. The first contribution is to accurately model two indoor environments; this accelerates the analysis by avoiding long durations needed for real measurements. In addition, the Maximum Likelihood Estimator (MLE) is applied as an RSS combining technique, in the aim to mitigate signals fluctuations.

Figure I. 1

 1 Figure I.1 Research linesThe thesis is derived as follows:

  contrary to localization systems based on vision, electromagnetic waves propagate through the environment allowing localization in Non-Line-of-Sight (NLoS) scenarios. Thanks to this characteristic, RF-based positioning systems have a larger coverage area and need less hardware compared to other systems. This chapter begins with presenting the radio frequency positioning metrics in section 1.2. The different techniques used in IPS are discussed in section 1.3. Section 1.4 is dedicated to provide an overview of existing localization technologies and their limitations. Then, major

  classified into: time as Time of Arrival (ToA) or Time Difference of Arrival (TDoA), angle such as Angle of Arrival (AoA), Phase of Arrival (PoA) or Phase Difference of Arrival (PDoA) and power measurements or Received Signal Strength (RSS).

  Time of Arrival (ToA) or Time of Flight (ToF) method is based on a theoretical propagation model of a radio frequency signal. The distance between the transmitting antenna (Tx) and the receiving antenna (Rx) can be determined by measuring the travel time of the signal between them. This distance is estimated by multiplying the ToA by the light celerity. The location of the receiving antenna (Rx) can be deduced using ToA estimations from various reference transmitting antennas (Tx). The intersection of three signals and nonlinear leastsquares approaches are applied to get optimal errors, as shown in Figure 1.1.

Figure 1 . 1

 11 Figure 1.1 ToA-based localization

Figure 1 .

 1 Figure 1.6 illustrates the localization by triangulation with the AoA method. The receiving antenna indicates the target object or person to be located, and the transmitting antennas represent the location reference devices. The target location is estimated by the intersection point of the directional lines.

Figure 1 . 6

 16 Figure 1.6 The Triangulation Technique

  2D. The intersection of the three circles provides a common point or coverage area of received signals, as shown in Figure 1.7.

approximately 1 .

 1 5 to 2 meters in a laboratory. Recently,[33] suggests a deep learning-based device-free localization system using ZigBee. The target location is estimated via RSS. The mean error achieved is 53 centimeters in an office room with dimensions of 51.84 square meters.

  It uses Frequency Hopping to protect signals against other systems that operate within the same 2.4 GHz ISM band. The communication range is about 10 centimeters to 15 meters, depending on the propagation factors such as LoS, material coverage, and antennas configuration [29]. This technology is embedded in most devices such as smartphones, smartwatches and laptops. Hence, Bluetooth can be reused by localization architectures instead of installing additional hardware. In fact, the Bluetooth-based ILSs can use the RSS technique to estimate the target's location. However, they suffer from the instability of the RSS values due to multipath in indoor environments; this increases the positioning latency by around 10 to 30 seconds, the power consumption, and localization

  develops a BLEbased ILS that allows to determine the target's location accurately in a museum. The positioning scenario considers that visitors are equipped with BLE equipment. People location is estimated by applying a Non-Linear Least Square (NLLS) algorithm in the considered environment. Results, obtained by measurements, show a position accuracy in the order of 2 meters.Wireless FidelityWireless Fidelity (WiFi) is supported by the IEEE 802.11 standard, it operates in the 2.4 GHz ISM band. Similar to other RF technologies, WiFi provides also an effective cost and low power consumption solution. It has a communication range about 100 meters in outdoor environments. In addition, WiFi (IEEE 802.11a,h operating at 900 MHz) range is increased to cover 1 kilometer for IoT applications outdoors[1]. WiFi becomes an increasingly common infrastructure in many buildings and can be an ideal candidate for ILSs because most of current smart devices such as watches, phones and laptops are WiFi enabled. In the same context, RSS, ToA, AoA positioning metrics can be used for WiFi-based localization system.

Figure 1 .

 1 Figure 1.10 Typical architecture for WIFI-based localization system

Figure 1 .

 1 Figure 1.11 Typical architecture of Passive RFID systems

Chapter 2 RFID

 2 Based Localization System Introduction The RFID technology has shown an importance for indoor localization services, presenting a low cost, high coverage and fast readability. Several ILSs based on RFID have been proposed [1]-[3]. They can be categorized into two types: reader-based and tag-based localization.

  proposes a passive RFID-based ILS with Dielectric Resonator (DR) tags with an operating bandwidth of 100 MHz. The proposed system uses the potential of the largescale Multiple Input Multiple Output (MIMO) technology. The DR tag is composed of an array of DR elements with a unique resonance frequency. These passive tags are designed to work as reference tags. The target location is estimated by applying the Weighted Linear Least-Squares (WLLS) estimator combined with the optimal large scale MIMO-based ranging technique. This RFID localization architecture produces high location accuracy of around 75centimeters in an office environment of 300 square meters.[16] presents an RFID localization method that uses the interference of 121 reference tags to detect the location of the passive target tag. The position information is captured by measuring the phase difference of reference tags. The frequency ranges from 920.625 MHz to 924.375 MHz. The location accuracy achieved is less than 6 centimeters in an experimental lab environment.

  presents an IPS-based on AoA and PDoA using Weighted Least Squares combined with Residual Weighted (WLS-RW) algorithm. This system performs with passive UHF-RFID tags in NLoS indoors. To distinguish multipath signals, an antenna array is used to find the strongest path based on RSS values. According to simulation results, this localization system can improve the location accuracy to reach 20 centimeters with a probability of 90%, in a 100 square meters' modeled room.Looking at systems' complexity,[20] investigates the RFID localization systems based on fingerprinting. RSS data, collected by 6 readers, are from 619 passive UHF RFID reference tags, deployed in a square warehouse of 124 square meters. To know the RSS spatial distribution, during the offline stage, a deep learning algorithm called Deep Belief Network (DBN) is designed. During the online or positioning stage, the target location is determined.

Figure 2 . 2

 22 Figure 2.2 The geometric and the distributed metallic elements in the classroom environment

Figure 2 . 3 Figure 2 . 4

 2324 Figure 2.3 UHF RFID Based Localization Architecture

Figure 2 . 5

 25 Figure 2.5 The Conventional RFID based localization system's Flowchart

  of positions, which are symmetrically distributed in both sides of the classroom. Only one active RFID tag is used as a reference, to reduce the offline complexity. It is placed on the center of the front wall of the classroom as shown (Figure 2.11).

Figure 2 . 6

 26 Figure 2.6 Two-dimensional floor map of the experiment site (Offline Stage)

Figure 2 . 7

 27 Figure 2.7 The Pioneer 3-DX Mobile Robot

Figure 2 . 8

 28 Figure 2.8 The diagram of Straight Line

Figure 2 . 9

 29 Figure 2.9 Clockwise and Counterclockwise rotation error

Figure 2 .

 2 Figure2.9 shows that when the robot turns clockwise, it can almost do 360° whereas it rotates 359.1° on average. However, counterclockwise, the robot tends to rotate more than 360°

  indoor localization system. During the offline and online stages, 200 and 20 RSS values are respectively collected at each position. They are combined via the averaging technique.Figure 2.11 presents the flowchart of the localization system based on average RSSs.

Figure 2 .

 2 Figure 2.11 Flowchart of the ILS based on the Average Technique

  It worth recalling that 200 and 20 RSS values are respectively collected at each position, during the offline and online stages. As the transmission is non-continuous, the 20 acquisitions are collected in 4 seconds, within the online stage. Whereas, around 40 seconds are needed to collect the 200 samples, in the offline stage. Due to the transmission intermittence and long acquisition duration, the propagation channel, between the tag and the reader, can be considered as non-stationary.

Figure 2 .

 2 Figure 2.14 (a) Coin ID RFID tag and (b) UTP Diff 2 RFID reader

Chapter 3

 3 Environment and Hardware Modeling Introduction Indoor scenarios represent one of the most complex geometries with exterior walls, windows, doors, etc. On shelves software like WinProp are well adapted to study the coverage of propagating signals in such indoor environments, hence reducing the need for costly measurements.Considering exhaustively all materials' parameters such as Fresnel parameters and all empirical loss coefficients, predictions of the propagative behavior of signals can be provided in a precise way. It is important to keep in mind that indoor building materials are prerequisite to simulate the environment. Hence, an accurate 3D environment was designed and validated by an in-depth comparison of received power values obtained via a campaign of real measurements.

  Winprop contains several tools (WallMan, AMan, ProMan) and different features that provide a wide interface for the user to present simulations. WallMan is used to design the environment and define the building database. AMan is mainly focused on antennas configuration. Then, signals coverage is characterized with ProMan. ProMan also includes empirical and semi-empirical models e.g. 3D Ray-Tracing and the Dominant Path Model (DPM).

Figure 3 . 2

 32 Figure 3.2 Indoor site of experiments in the hall

Figure 3 .

 3 Figure 3.6 x-z, y-z, x-y and 3D view of the classroom in WallMan

  designed. Its radiation pattern is taken into consideration in order to obtain signals propagation closest to the reality. Vertical and horizontal antenna patterns are available from manufacturers; they describe the antenna's radiation in ideal environments (Figure 3.8).

Figure 3 . 8

 38 Figure 3.8 Horizontal and vertical radiation patterns of the tag's antenna

Figure 3 . 1 Figure 3 .

 313 Figure 3.12 3D radiation pattern of the RFID reader antenna 1

Figure 3 . 14 illustrates

 314 an example of many possible rays between the transmitter and receiver. Using DPM, the computation of the received power depends on the accuracy of the indoor scenario description already defined in WallMan. This model performs with light computational resources.

Figure 3 .

 3 Figure 3.14 Multipath propagation and the dominant path indoor

Figure 3 .

 3 Figure3.17 Ray-Tracing Versus Ray-Launching model[18]

Figure 3 .

 3 Figure 3.18 represents the algorithm that should be followed to examine the signals propagation in the indoor environment.

Figure 3 .

 3 Figure 3.18 Flowchart for the coverage prediction and network planning with ProMan

Figure 3 .

 3 Figure 3.19 3D Layout of the Simulated classroom by ProMan

Figure 3 .

 3 Figure 3.23 Measured and simulated Power values over the central track A90 of the classroom

  The following experiment is performed over the seven tracks A30 till A150 of the classroom environment (Figure3.22). The estimated (tag-reader) distance errors are determined, by applying the OSM, over each track independently for both scenarios (real and simulation).

Figure 3 .

 3 24 presents the Cumulative Distribution Function (CDF) for the real and simulated estimated distance errors over the sixty-four considered positions.

Figure 3 .

 3 Figure 3.24 CDF of estimated distance errors for the real measurements and simulations

  algorithm for the RFID tag configuration, and MoM for the RFID reader modeling, were clearly elaborated. For instance, the radiation pattern of the RFID tag antenna is configured through AMan. The RFID reader is designed by FEKO software and then used within WinProp to get accurate values of the power received by the reader. Besides, all material parameters that constitute the classroom and hall environments, are defined. The signal propagation characteristics, at 433 MHz, are analyzed with a single RFID tag fixed on the wall of the classroom environment, and with five tags over the hall. Simulations were achieved using the 3D Ray-Tracing model of WinProp. Experimental and simulated results had similar behavior with a maximum deviation of 10 centimeters over the classroom environment. However, differences over the hall environment exceed 0.5 dB at particular positions due to the obstructed NLoS.Finally, it has been noticed that our measurement campaign in the hall was not sufficient and representative for indoor positioning. Hence, the following research is conducted only in the classroom environment. is then estimated, by applying the new propagation channel model (DOSM or DOSSOM) associated with WAAF and followed by Multilateration.First, in section 4.2, RSS sets collected within the offline stage are examined. To examine and overcome the fluctuations impact, different RSS combining techniques are adopted; the conventional averaging as well as the Maximum Likelihood Estimator (MLE) as combining technique are elaborated in section 4.3. Indoor propagation models found in the literature, as well as the proposed approaches (DOSM and DOSSOM) are then introduced and analyzed in sections 4.4 and 4.5, respectively. The Weighted Average Attenuation Factor (WAAF) applied for an accurate environmental calibration is presented in section 4.6. Section 4.7 is dedicated to capitalize the overall proposed ILS. It is followed by localization assessments, based on the multilateration technique, in section 4.8. Once the complete proposed ILS validated by positioning errors results', section 4.9 ends the chapter with a detailed conclusion.

  This subsection is dedicated to study the RSS fluctuations in the classroom during the offline stage. The received samples, at sixty-four reference positions distributed over the seven tracks A30 until A150 (Figure 4.1) are examined.

Figure 4 . 1

 41 Figure 4.1 Two-dimensional Layout of the classroom environment (Offline Stage)

4 . 3 ,

 43 several ILSs found in the literature apply the averaging technique to combine the received 𝑅𝑅𝑅𝑅𝑅𝑅 𝑇𝑇 values. The mean 𝑅𝑅𝑅𝑅𝑅𝑅 � is given by: K is the number of RSS values collected at each position and 𝑅𝑅𝑅𝑅𝑅𝑅 𝑇𝑇 is the i th received signal strength at each position.

hall and 16

 16 rooms. Path loss is given by equation (4.10) for LoS and by equation (4.11) for NLoS: 𝑃𝑃𝐿𝐿(𝑑𝑑) = 16.9𝑙𝑙𝑙𝑙𝑎𝑎 10 (𝑑𝑑) + 20𝑙𝑙𝑙𝑙𝑎𝑎10 (𝑓𝑓) + 32.8 (4.10) 𝑃𝑃𝐿𝐿(𝑑𝑑) = 43.3𝑙𝑙𝑙𝑙𝑎𝑎 10 (𝑑𝑑) + 20𝑙𝑙𝑙𝑙𝑎𝑎10 (𝑓𝑓) + 11.5 (4.11)

frequencies 5 and 5 . 8

 58 GHz[3]. The path loss is computed as in equation (4.12). Within buildings, this propagation model incorporates a linear component of loss, proportional to the number of walls penetrated, plus a more complex term which depends on the number of floors penetrated, producing a loss that increases more slowly as additional floors are included.𝑃𝑃𝐿𝐿 𝑀𝑀𝑊𝑊𝐹𝐹 (𝑑𝑑) = 𝑃𝑃𝐿𝐿 0 + 10𝑛𝑛𝑙𝑙𝑙𝑙𝑎𝑎 10 (𝑑𝑑) + � � 𝐿𝐿 𝑤𝑤𝑇𝑇𝑤𝑤 +

  As signals propagating in our case study have a frequency of 433 MHz, the two empirical indoor propagation models OSM and SOPPM, found in the literature and presented in section 4.4, as well as the two proposed models described in the previous subsections 4.5.1 and 4.5.2 are applied on the received power values collected, during the offline stage, over the three tracks A60, A90 and A120 (Figure 4.1). These three tracks were selected because they include a sufficient number of reference positions to better reflect the signal attenuation in the considered environment. Power variations are shown in Figure 4.6, Figure 4.7 and Figure 4.8 respectively.

Figure 4 . 6 Figure 4 . 7 Figure 4 . 8

 464748 Figure 4.6 Received Power by the different empirical propagation models over the track A60

  During the offline stage, a defined number of RSS values are collected. These samples are combined via the MLE. This combined RSS value is then converted into received power in dBm. In order to determine the attenuation parameters that cover the whole classroom environment, the proposed empirical indoor propagation model DOSM or DOSSOM, associated with the weighted average constant parameters is applied.The attenuation parameters using DOSM propagation with WAAF are previously defined in equations (4.28) and (4.29). In addition, the attenuation coefficients using DOSSOM propagation with WAAF are already expressed in equations (4.31) and (4.32).Within the online stage, another defined number of RSS samples are collected at each unknown location and combined with the same technique (MLE) used during the calibration phase. The best RSS sample is further converted into received power in dBm. The RFID reader

Figure 4 .

 4 Figure 4.11 presents the CDF of the obtained positions errors' with the two calibration parameters AF90 and WAAF while applying the DOSM as indoor propagation model.

Figure 4 .

 4 Figure 4.11 Comparative CDF for the positions errors with DOSM

Figure 4 .

 4 Figure 4.12 compares the CDF of the positions errors' obtained while applying the DOSSOM as a propagation model.

Figure 4 .

 4 Figure 4.12 Comparative CDF for the positions errors with DOSSOM

Figure 4 .

 4 13 and Figure 4.14, illustrate the positions errors' achieved through the two different attenuation factors: AF90 and WAAF. Experiments were performed with the proposed indoor propagation models DOSM and DOSSOM.

Figure 4 .

 4 Figure 4.13 presents the CDF of the obtained positions errors' using the calibration parameters AF90 and WAAF independently while applying DOSM.

Figure 4 .

 4 Figure 4.13 CDFs for the positions errors with the average and MLE using DOSM

Figure 4 .

 4 Figure 4.13 (b) represents the CDF of the positions errors' using DOSM with WAAF. The positions errors', at 90% CDF, are 1.5 and 1.1 meters with the averaging technique and

Figure 5 . 1

 51 Figure 5.1 MISO Communication System

Figure 5 . 4

 54 Figure 5.4 Different radii for the constellation of tags

Figure 5 .

 5 5 (a)), diamond shape with four tags (Figure 5.5 (b)), square cross with five tags (Figure 5.5 (c)) and pentagon with five tags (Figure 5.5 (d)).

Figure 5 . 5

 55 Figure 5.5 Constellation of tags with different shapes and different number of tags

Figure 5 . 6

 56 Figure 5.6 Three-dimensional layout of the classroom

  behaviors separately in order to show that each tag's signal takes a different path to reach the reader due to multipath in the classroom. Moreover, comparison is realized on distance errors, obtained by the single tag and constellation, following the block diagram illustrated in Figure 5.7.

Figure 5 . 7

 57 Figure 5.7 Steps for the estimated distance error calculation

Furthermore, Figure 5 . 8 (

 58 a), Figure 5.9 (a) and Figure 5.10 (a) present the estimated distances errors' distribution over the three tracks A60, A90 and A120 for the single tag scenario. Those obtained by the optimal constellation of tags and by each tag of the optimal constellation independently are shown in Figure 5.8 (b), Figure 5.9 (b) and Figure 5.10 (b).

Figure 5 . 8 Figure 5 . 9 5 . 3 . 1 . 2 Simulations

 58595312 Figure 5.8 Distance errors for single tag and constellation of tags scenarios over track A60

Figure 5 .

 5 Figure 5.11 single tag and constellation of tags simulated scenarios

Figure 5 .

 5 Figure 5.12 Two-dimensional layout of the classroom environment (online stage with constellation)

Figure 5 .

 5 Figure 5.13 Multilateration with the single tags' scenario

Figure 5 .

 5 16 presents CDF of positions errors' by applying DOSSOM with WAAF.

Figure 5 .

 5 Figure 5.17 presents the CDF of positions errors' while applying DOSM associated with WAAF in the classroom environment.

Figure 5 .

 5 Figure 5.17 CDF for positions errors via MLE using DOSM and WAAF

  De nombreux modèles de propagation indoor empiriques, présents dans la littérature, ont été dédiés à améliorer la précision des systèmes de localisation. Parmi ces modèles, on peut 𝑛𝑛 𝑇𝑇 𝑖𝑖 . 𝑙𝑙𝑙𝑙𝑎𝑎 10 (𝑑𝑑)+𝑋𝑋 𝑇𝑇 𝑖𝑖 𝑑𝑑 ≤ 3𝜆𝜆 𝑃𝑃 0 ′ -𝑎𝑎 𝑇𝑇 𝑖𝑖 . 𝑙𝑙𝑙𝑙𝑎𝑎 10 (𝑑𝑑) 2 -𝑏𝑏 𝑇𝑇 𝑖𝑖 . 𝑙𝑙𝑙𝑙𝑎𝑎 10 (𝑑𝑑) -𝑐𝑐 𝑇𝑇 𝑛𝑛 1𝑇𝑇𝑇𝑇 et 𝑛𝑛 2𝑇𝑇𝑇𝑇 représentent les coefficients d'atténuation du trajet 𝑇𝑇 𝑇𝑇 . Les termes 𝑋𝑋 1𝑇𝑇 𝑖𝑖 et 𝑋𝑋 2𝑇𝑇 𝑖𝑖 décrivent une variable log-normale pour l'erreur de la puissance reçue tout au long de la première partie de chaque trajet modélisé par la variation de la pente (premier 𝑛𝑛 𝑇𝑇𝑇𝑇 est le coefficient d'atténuation du trajet qui correspond à la première partie de chaque trajet. 𝑋𝑋 𝑇𝑇 𝑖𝑖 présente la variation log-normale de la puissance reçue modélisée par la variation de la pente. 𝑎𝑎 𝑇𝑇 𝑖𝑖 , 𝑏𝑏 𝑇𝑇 𝑖𝑖 et 𝑐𝑐 𝑇𝑇 𝑖𝑖 sont les paramètres du polynôme du second ordre qui correspond à la seconde partie de chaque trajet.En effet, notre système de localisation RFID de base utilise les paramètres d'atténuation du trajet A90 abrégés par 𝑛𝑛 𝑇𝑇90 , 𝑎𝑎 𝑇𝑇 90 , 𝑏𝑏 𝑇𝑇 90 et 𝑐𝑐 𝑇𝑇 90 .Les deux modèles que nous avons proposés ont été comparés aux modèles de propagation empiriques déjà présents dans la littérature. Les résultats obtenus montrent que DOSSOM présente une amélioration de l'erreur d'au moins un mètre.

	Pour le modèle DOSSOM,
	ordre).
	xix

citer COST 231 ou modèle à pente unique (One Slope Model, OSM), modèle de propagation de second ordre (Second order Propagation Propagation Model, SOPPM). Dans nos travaux, les modèles de propagation proposés sont empiriques. Nous les avons appelés : le modèle à double pente (Double One Slope Model DOSM) et le modèle à double pente et de second ordre (Double One Slope Second Order Model DOSSOM). Le modèle DOSM est alors représenté par : 𝑃𝑃 𝑟𝑟 (𝑑𝑑) = � 𝑃𝑃 0 -10. 𝑛𝑛 1𝑇𝑇 𝑖𝑖 . 𝑙𝑙𝑙𝑙𝑎𝑎 10 (𝑑𝑑)+𝑋𝑋 1𝑇𝑇 𝑖𝑖 𝑑𝑑 ≤ 3𝜆𝜆 𝑃𝑃 0 ′ -10. 𝑛𝑛 2𝑇𝑇 𝑖𝑖 . 𝑙𝑙𝑙𝑙𝑎𝑎 10 (𝑑𝑑)+𝑋𝑋 2𝑇𝑇 𝑖𝑖 𝑑𝑑 > 3𝜆𝜆 (R.3) Et, le modèle DOSSOM est défini par :

𝑃𝑃 𝑟𝑟 (𝑑𝑑) = � 𝑃𝑃 0 -10. 𝑖𝑖 𝑑𝑑 > 3𝜆𝜆 (R.4)

𝑃𝑃 𝑟𝑟 (𝑑𝑑) est la puissance reçue en dBm à une distance (d) en mètres, 𝑃𝑃 0 est la puissance reçue à la distance de 1 mètre. 𝑃𝑃 0 ′ est la puissance reçue à 3𝜆𝜆 en dBm. i représente l'indice du trajet correspondant parmi les sept trajets utilisés lors de la phase de calibrage.

Pour le modèle DOSM,

  n w1 est le facteur d'atténuation moyen pondéré correspondant à la première partie du trajet. n T i est le facteur d'atténuation associé à chaque trajet. A w2 , B w2 et C w2 représentent les paramètres de la moyenne pondérée correspondants au modèle de propagation du second ordre (DOSSOM). a T i , b T i et c T i sont les paramètres associés à chaque trajet. N′ Total et N′′ Total représentent le nombre total de positions de chacune des deux parties. N′ Pos

			T i � et N′′ Pos � sont T i
	les nombres de positions sur chaque partie de chaque trajet.
	Les coefficients WAAF sont directement proportionnels au nombre de points
	considérés dans le calibrage. Ils prennent en compte toutes les positions afin de couvrir tout
	l'environnement.
	7	𝑁𝑁′′𝑃𝑃𝑃𝑃𝑃𝑃 � 𝑇𝑇 𝑖𝑖
	𝑇𝑇=1	𝑁𝑁′′ 𝑇𝑇𝑃𝑃𝑇𝑇𝑇𝑇𝑇𝑇
	𝐵𝐵 𝑤𝑤2 = � 𝑏𝑏 𝑇𝑇 𝑖𝑖 7 𝑇𝑇=1	𝑁𝑁′′𝑃𝑃𝑃𝑃𝑃𝑃 � 𝑇𝑇 𝑖𝑖 𝑁𝑁′′ 𝑇𝑇𝑃𝑃𝑇𝑇𝑇𝑇𝑇𝑇	(R.6)
	7 𝐶𝐶 𝑤𝑤2 = � 𝑐𝑐 𝑇𝑇 𝑖𝑖 𝑇𝑇=1	𝑁𝑁′′𝑃𝑃𝑃𝑃𝑃𝑃 � 𝑇𝑇 𝑖𝑖 𝑁𝑁′′ 𝑇𝑇𝑃𝑃𝑇𝑇𝑇𝑇𝑇𝑇

Avec le modèle de propagation DOSSOM associé aux facteurs d'atténuation (WAAF), la puissance reçue par le lecteur RFID est exprimée par l'équation suivante :

Table 1 .

 1 1 Comparison of positioning Metrics ......................................................................... Table 1.2 Passive RFID Frequency Bands .............................................................................. Table 1.3 Summary of the optimal ILS performance for each technology ............................. Table 1.4 Indoor Positioning Systems Characteristics ............................................................ Table 1.5 Comparative Table for the different Radio Frequency Based-ILSs ........................ Table 2.1 Summary of RFID-based ILSs' Drawbacks and Solutions ..................................... Table 2.2 Comparison of UHF RFID Localization schemes ................................................... Table 2.3 Wheels' Deviations over Straight Path .................................................................... Table 2.4 Difference between Wheels velocity ....................................................................... Table 2.5 Robot Angles' deviation .......................................................................................... Table 3.1 Fresnel coefficients of the materials in the two environments ................................ Table 3.2 Empirical Losses of the included materials in both environments .......................... Table 3.3 Comparison between the WinProp Indoor Propagation models .............................. Table 3.4 Measured and Simulated Estimated Distance Errors over the central track A90 of the classroom ........................................................................................................................... Table 3.5 Measured and Simulated Received Power Analysis over A90 and A'90 ................ Table 4.1 MDE with Average RSS over the three tracks A60, A90 and A120 ..................... Table 4.2 Comparison of the Estimated Distance Errors via AF90 and WAAF AttenuationFactors .................................................................................................................................... Table 5.1 The mean estimated distance errors for different radii of constellations with OSM ................................................................................................................................................ Table 5.2 The mean estimated distance errors for different constellations with OSM .......... Table 5.3 Mean estimated distance errors based on real measurements ................................ Table 5.4 The mean estimated distance errors by simulation ................................................

xxviii List

of Figures Figure

  R.1 Objectifs et contributions ..................................................................................... xiii Figure R.2 Schéma fonctionnel du système de localisation RFID de base ............................ xiv Figure R.3 Plan d'expérimentation des phases hors et en ligne (Offline and Online phase) ... xv Figure R.4 Outils de simulation .............................................................................................. xvi Figure R.5 Disposition 3D des environnements réels et simulés........................................... xvii Figure R.6 CDF des erreurs de position avec la moyenne et MLE ........................................ xxi Figure R.7 Constellation avec différentes formes et nombres de balises RFID .................... xxii Figure R.8 CDF des erreurs de position avec constellations ou balise unitaire ................... xxiii Figure I.1 Research lines ............................................................................................................ 2 Figure 1.1 ToA-based localization ............................................................................................. 6 Figure 1.2 TDoA-based localization .......................................................................................... 7 Figure 1.3 Positioning based on AoA measurement .................................................................. 8 Figure 1.4 RSSI-based localization............................................................................................ 9 Figure 1.5 Localization by Proximity ...................................................................................... 11 Figure 1.6 The Triangulation Technique ................................................................................. 12 Figure 1.7 The Multi/Trilateration Technique ......................................................................... 13 Figure 1.8 Fingerprinting ......................................................................................................... 14 Figure 1.9 Typical architecture for BLE-based localization system [39] ................................ 17Figure 1.10 Typical architecture for WIFI-based localization system .................................... 18 Figure 1.11 Typical architecture of Passive RFID systems ..................................................... 21 The Pioneer 3-DX Mobile Robot ........................................................................... 42 Figure 2.8 The diagram of Straight Line ................................................................................. 42 Figure 2.9 Clockwise and Counterclockwise rotation error .................................................... 44 Figure 2.10 Diagram of a square path clockwise and counterclockwise ................................. 45 Figure 2.11 Flowchart of the ILS based on the Average Technique ....................................... 46 Figure 2.12 Positioning by Multilateration ............................................................................Layout of the classroom environment .............................................................. 61 Figure 3.4 3D Layout of the hall environment ........................................................................ 61 Figure 3.5 Flowchart for the generation of vector databases ................................................... 62 Figure 3.6 x-z, y-z, x-y and 3D view of the classroom in WallMan ....................................... 65 xxix Figure 3.7 x-z, y-z, x-y and 3D view of the hall in WallMan .................................................. Figure 3.8 Horizontal and vertical radiation patterns of the tag's antenna .............................. Figure 3.9 3D radiation pattern of the tag antenna with AMan ............................................... Figure 3.10 Feko numerical methods overview [15] ............................................................... Figure 3.11 Meshed structure for the RFID reader box ........................................................... Launching model [18] ................................................. Figure 3.18 Flowchart for the coverage prediction and network planning with ProMan ........ Figure 3.19 3D Layout of the Simulated classroom by ProMan ............................................. Figure 3.20 3D Layout of the Simulated hall presented by ProMan ....................................... Figure 3.21 Block diagram of the distance error calculation ................................................... Figure 3.22 2D Layout of the classroom ................................................................................. Figure 3.23 Measured and simulated Power values over the central track A90 of the classroom ................................................................................................................................. Figure 3.24 CDF of estimated distance errors for the real measurements and simulations ..... Figure 3.25 2D Layout of the hall ............................................................................................Figure 4.1 Two-dimensional Layout of the classroom environment (Offline Stage) ..............Figure 4.2 RSS Standard Deviation over the classroom ..........................................................Figure 4.3 Histograms of 200 RSSI collected at 3 meters over 4 different tracks ..................Figure 4.4 RSS values combined by MLE in offline and online stages .................................. Received Power by the different empirical propagation models over the track A90 ................................................................................................................................................Figure 4.8 Received Power by the different empirical propagation models over the track A120 ....................................................................................................................................... Figure 4.9 Block diagram of the complete ILS based on MLE with WAAF ........................Figure 4.10 Two-dimensional layout of the classroom environment (Online Stage) ............Figure 4.11 Comparative CDF for the positions errors with DOSM .....................................Figure 4.12 Comparative CDF for the positions errors with DOSSOM................................Figure 4.13 CDFs for the positions errors with the average and MLE using DOSM............Figure 4.14 CDFs for the positions errors with the average and MLE using DOSSOM ......Figure 5.1 MISO Communication System............................................................................. Steps for the estimated distance error calculation ................................................Figure 5.8 Distance errors for single tag and constellation of tags scenarios over track A60 ................................................................................................................................................ Distance errors for single tag and constellation of tags scenarios over track A90 ................................................................................................................................................ Figure 5.10 Distance errors for single tag and constellation of tags scenarios over track A120 ................................................................................................................................................ Figure 5.11 single tag and constellation of tags simulated scenarios .................................... Figure 5.12 Two-dimensional layout of the classroom environment (online stage with constellation) ..........................................................................................................................Figure 5.13 Multilateration with the single tags' scenario ....................................................Figure 5.14 Multilateration with the constellation of tags scenario .......................................Figure 5.15 CDF for positions errors by RSS Averaging using DOSM and WAAF ............ Figure 5.16 CDF for positions errors via RSS Averaging using DOSSOM and WAAF ...... Figure 5.17 CDF for positions errors via MLE using DOSM and WAAF ............................ Figure 5.18 CDF for positions errors via MLE using DOSSOM and WAAF .......................

	Figure 1.12 Typical architecture of Active RFID systems ...................................................... 22 Figure 2.1 Indoor site of experiments in the classroom ........................................................... 37 Figure 2.2 The geometric and the distributed metallic elements in the classroom environment .................................................................................................................................................. 38 Figure 2.3 UHF RFID Based Localization Architecture ......................................................... 38 Figure 2.4 General Overview of the Conventional UHF RFID localization system ............... 39 Figure 2.5 The Conventional RFID based localization system's Flowchart ........................... 39 Figure 2.6 Two-dimensional floor map of the experiment site (Offline Stage) ...................... 40 Figure 2.7 .. 48 Figure 2.13 Positioning by Multilateration in Two-dimensional Layout ................................ 50 Figure 2.14 (a) Coin ID RFID tag and (b) UTP Diff 2 RFID reader ....................................... 51 Figure 2.15 RSSI vs Power [dBm] .......................................................................................... 52 Figure 3.1 Indoor site of experiments in the classroom ........................................................... 59 Figure 3.2 Indoor site of experiments in the hall ..................................................................... 60 Figure 3.15 Scenario with transmitter Tx, receiver Rx and different types of corners [7] ...... Figure 3.16 Tree structure of the DPM [7] .............................................................................. Figure 3.17 Ray-Tracing Versus Ray-Figure 4.5 Received Power measurements over track A90 ................................................... Figure 4.6 Received Power by the different empirical propagation models over the track A60 ................................................................................................................................................ Figure 4.7 Figure 5.2 Constellation of four RFID tags ........................................................................... Figure 5.3 Two-dimensional layout of the classroom environment ...................................... Figure 5.4 Different radii for the constellation of tags .......................................................... Figure 5.5 Constellation of tags with different shapes and different number of tags ............ Figure 5.6 Three-dimensional layout of the classroom ......................................................... IoT Internet of Things KNN K-nearest-neighbor xxxii RSS Received Signal Strength xxxiii WIFI Wireless Fidelity xxxiv Figure 5.7 xxx LED Light Emitting Diode LF Low Frequency LiFi Light Fidelity RSSI Received Signal Strength Indicator RTLS Real-Time Location System Rx Receiving Antenna WPAN Wireless Personal Area Networks WSN Wireless Sensor Network 2D Two-dimensional Figure 5.9 AoA Angle of Arrival AMan Antenna Manager AM Arithmetic Mean BLE Bluetooth Low Energy BI Bilinear Interpolation CDF Cumulative Distribution Function DOSM Dual One Slope Model DOSSOM Dual One Slope with Second Order polynomial Model DBN Deep Belief Network DPM Dominant Path Model DR Dielectric Resonator EXP Exponential Interpolation GPS Global Positioning System GTD Geometric Theory of Diffraction HF High Frequency HPI Horizontal Projection Interpolation (HPI) ILS Indoor Localization System IPS Indoor Positioning System IR InfraRed IRT Intelligent Ray Tracing ISM Industrial, Scientific and Medical MDE Mean Distance Error MFSM Modified Free Space Model MISO Multiple Input Single Output MIMO Multiple Input Multiple Output MLE Maximum Likelihood Estimator MCL Monte Carlo localization (MCL) MoM Method of Moment MWMF Multi-Wall Multi-Floor Model NLoS Non-Line-of-Sight NLLS Non-Linear Least Square NMDS Nonmetric Multi-Dimensional Scaling OSM One Slope Model PDR Pedestrian Dead Reckoning PDoA Phase Difference of Arrival PoA Phase of Arrival ProMan Propagation Manager PSO Particle Swarm Optimization RF Radio Frequency RFID Radio Frequency Identification RPD Received Power Difference SIMO Single Input Multiple Output SNR Signal-to-Noise Ratio SMP Smallest M-vertex Polygon SOPPM Second Order Polynomial Propagation Model SRT Standard Ray Tracing Std Standard Deviation SVM Support Vector Machine ToA Time of Arrival TDoA Time Difference of Arrival ToF Time of Flight Tx Transmitting Antenna UHF Ultra-High Frequency UTD Uniform Theory of Diffraction UWB Ultra-Wide Band WAAF Weighted Average Attenuation Factor WallMan Wall Manager WBI Weighted Bilinear Interpolation WLAN Wireless Local Area Network WLLS Weighted Linear Least-Squares WLS-RW Weighted Least Squares combined with Residual Weighted Figure 3.3 3D Figure 3.12 3D radiation pattern of the RFID reader antenna 1 .............................................. KNN K-Nearest-Neighbor SHF Super High Frequency Figure 3.14 Multipath propagation and the dominant path indoor .......................................... AFC Attenuation Factor Classic Model Figure 3.13 3D radiation pattern of the RFID reader antenna 2 .............................................. LoS Line-of-Sight SCCL Self-Calibrating Centroid Localization 3D Three-dimensional

xxxi

Nomenclature and Abbreviations

  mentions that IR positioning system based on AoA present an accurate target's location. IR signals do not penetrate through walls, therefore IR ILS require a LoS communication between transmitter and receiver to perform properly.

These systems are beyond the scope of our research work. It is worth recalling that RF technology offers pervasive and low-cost solutions. We focus on radio communication technologies such as ZigBee, Bluetooth, Wireless Fidelity (WIFI), Ultra-Wideband (UWB)

Table 1 .

 1

		2 Passive RFID Frequency Bands
	Frequency Bands	Common Frequency	Communication Range
	LF	30 -150 kHz	20 centimeters
	HF	3-30 MHz	10 centimeters
	UHF	433 MHz 868 -915 MHz	10 meters Up to 10 meters
	Microwave	2.4 -2.5 GHz 5.8 GHz	3 meters 3 meters
	Dense RFID tags or readers need to be deployed to improve the location accuracy in
	indoor environments. For instance, [50] applies WallSence algorithm to detect directly the
	target's location by applying Particle Swarm Optimization (PSO) with a novel weighted
	function. In fact, the localization algorithm is associated with two orthogonal tag arrays. The

RFID reader operates at 920 MHz to 926 MHz band and provide the phase acquisitions for each successful identification, and reach an optimal location accuracy of 24 centimeters using 50 passive RFID tags in a large empty room of 102.3 square meters.

Table 1 .

 1 3 Summary of the optimal ILS performance for each technology

	Technology	Covered Area [𝒎𝒎 𝟐𝟐 ]	Accuracy [m]
	ZigBee [33]	51.84	0.53
	Bluetooth [38]	78.87	1.15
	WiFi [43]	128	0.87
	UWB [45]	60.5	0.33
	Passive [50]	102.3	0.24
	RFID
	Active [51]	1600	3.4

In short, the RF technology is a critical factor affecting the ILS accuracy and performance in complex indoor environments. Among these technologies, ZigBee, Bluetooth and WiFi present limited communication ranges and low locations accuracy. The UWB technology presents high location accuracy with expensive cost. However, RFID-based localization systems offer affordable cost, high-range communication, fast readability and accurate localization solutions

[7]

.

Table 1 .

 1 5 Comparative Table for the different Radio Frequency Based-ILSs

	Disadvantages		High maintenance costs	Not Secure		Limited range		High variance signal	High power consumption	Very expensive for	end user		Limited accuracy and	range with passive tags;	Reasonable cost with	active tags
	Advantages		Low cost	Wide range	Low power consumption	Low cost	Low power consumption	Low cost	Large range	High accuracy	High resistance against	interference	Low cost with passive	tags	High accuracy and wide	range with active tags
	Accuracy		2 meters			2 to 3 meters		2 meters		< 1 meter			< 1 meter with	active tag;	2 to 5 meters	with passive tag
	Indoor Maximum	Range	30 meters			15 meters		100 meters		< 100 meters			< 100 meters with	active tag;	2 meters with	passive tag
	Metrics		RSS			RSS; ToA;	TDoA; AoA	RSS; ToA;	TDoA; AoA	ToA; TDoA;	AoA		RSS; ToA;	TDoA; AoA
	Technology		ZigBee			Bluetooth		Wi-Fi		UWB			RFID

  Examined systems, in this subsection, are based either on passive or active RFID tags, and use several localization techniques.[8] proposes passive RFID-based ILS. It deploys 50 passive RFID tags in a large empty room of 102.3 square meters.[9] uses 64 active RFID tags operating at 433 MHz, in a 1600 square meters indoor environment.LANDMARC is the first feasible method that uses active RFID tags; they are usually placed in the form of a regular grid with a limited number of readers. Two different categories of tags are defined: reference and tracking tags. Reference tags are deployed and installed at known locations covering the environment. Tracking ones are attached to the moving objects.Readers receive RF signals from both tags categories. LANDMARC uses reference tags emitting the closest RSS values to those collected by the tracking tags. They are called candidate reference tags and are used to estimate the locations of the tracking tags. For instance,[10] introduces the LANDMARC concept as a solution; the RFID reader is operating at 308 MHz and the detection range of the used RFID active tags is 45.7 meters. The maximum error distance achieved is less than 2 meters, while 4 readers and 16 active reference tags are deployed to detect 8 active target tags in an indoor environment of 36 square meters. In the same context,

Table 2 .

 2 2 Comparison of UHF RFID Localization schemes

	Accuracy	[m]	0.24	0.06		0.2	1	3.4	2	0.75	0.45	0.75
		Technique Metric		WallSense PoA	Landmark PDoA		WLS-RW AoA, PDoA	DBN RSS	Filter RSS	Landmark RSS	Landmark RSS	Cocktail RSS	WLLS RSS
	Tags	Density/Square	meters	0.48	0.4		0.01	4.99	0.04	0.66	0.56	1.36	0.88
		Range [m]		Not specified	Not specified		Not specified	Not specified	20	45.7	Not specified	Not specified	Not specified
		Frequency Band		920-926 MHz	920.25-924.37	MHz	UHF	UHF	433 MHz	308 MHz	433 MHz	303.72 MHz	100 MHz
		RFID-based ILS		[8]	Passive RFID-[16]	based ILS [19]	[20]	[9]	[10]	Active RFID-[11] based ILS [13]	[15]

Table 2 .

 2 3 Wheels' Deviations over Straight Path

	Number of Tests	Test 1	Test 2	Test 3	Mean Deviation [cm]
	Deviation/1 m [cm]	6.78	6.75	6.72	6.75

Table 2 .

 2 4 Difference between Wheels velocity

	Number of Tests	Test 1	Test 2	Test 3
	|𝑽𝑽 𝒍𝒍 -𝑽𝑽 𝒓𝒓 | [mm/s]	0.04	0.01	0.03

Table 2 .

 2

			5 Robot Angles' deviation
	Angle	A1	A2	A3	A4
	Clockwise (α)	90.1°	93.6°	92.4°	88.4°
	Counterclockwise (β)	88.1°	88.6°	98.3°	81.1°

Table 3 .

 3 3 Comparison between the WinProp Indoor Propagation models

	Models	Materials Properties	Walls Properties	Computation time	Preprocessing Accuracy
	MFSM
	or	Not Considered Not Considered	Short	Not Required	Low
	OSM
	DPM	Considered	Considered	Short	Not Required Trade-off
	SRT	Considered	Considered	Large	Not Required	High
	IRT	Considered	Considered	Short	Required	High
	After presenting the characteristics of the indoor propagation scenarios, the Ray-

Tracing model, that considers all materials properties, is applied in our use cases. Particularly, we used Standard Ray-Tracing for its high accuracy. Despite the high efficiency of the Intelligent Ray-Tracing, it requires preprocessing of the environment database via WallMan.

Table 3 .

 3 According to the Mean Distance Error (MDE) presented in Table3.4, it can be noticed that measurements and simulations present very close results.

	4 Measured and Simulated Estimated Distance Errors over the central track
	A90 of the classroom
	Distance [m]	Measurements Distance Errors [m]	Simulations Distance Errors [m]
	0.5	0	0
	1	0.22	0.21
	1.5	0.82	0.83
	2	1.23	1.2
	2.5	1.98	2.03
	3	2.67	2.67
	3.5	1.25	1.28
	4	2.98	3.01
	4.5	4.13	4.15
	5	2.97	3.1
	5.5	1.67	1.72
	6	3.6	3.62
	6.5	4.9	4.86
	7	5.1	5.07
	MDE [m]	2.4	2.41

Table 3 .

 3 5 Measured and Simulated Received Power Analysis over A90 and A'90

		Track A90	Track A'90
	Tag Number
		RPD > 0.5 dB	Std [dB]	RPD > 0.5 dB	Std [dB]
	Tag 1	5	0.1122	11	0.1824
	Tag 2	2	0.2634	3	0.3891
	Tag 3	3	0.2566	9	0.2635
	Tag 4	5	0.3805	7	0.4105
	Tag 5	15	0.4746	13	0.5146
	Based on results in Table 3.5, it can be noticed that the standard deviation exceeds 0.3
	dB with tags 4, 5 over track A90, and with tags 3, 4 and 5 over track A'90. This deviation is
	due to many factors such as some metallic objects placed in the hall (around tags 2, 3, 4 and 5)

  1 meters. The proposed empirical Dual One Slope Propagation Model (DOSM) that fitted the collected received power for each track, is defined in the following equations (4.16): 𝑃𝑃 𝑟𝑟 (𝑑𝑑) = � 𝑃𝑃 0 -10. 𝑛𝑛 1𝑇𝑇 𝑖𝑖 . 𝑙𝑙𝑙𝑙𝑎𝑎 10 (𝑑𝑑)+𝑋𝑋 1𝑇𝑇 𝑖𝑖 𝑑𝑑 ≤ 3𝜆𝜆 𝑃𝑃 0 ′ -10. 𝑛𝑛 2𝑇𝑇 𝑖𝑖 . 𝑙𝑙𝑙𝑙𝑎𝑎 10 (𝑑𝑑)+𝑋𝑋 2𝑇𝑇 𝑖𝑖 𝑑𝑑 > 3𝜆𝜆 (4.16) 𝑃𝑃 𝑟𝑟 (𝑑𝑑) is the received power in dBm at the distance d in meters, 𝑃𝑃 0 is the received power at 1 meter in dBm. 𝑃𝑃 0 is the received power at 3𝜆𝜆 in dBm. i is the track index. The terms 𝑛𝑛 1𝑇𝑇 𝑖𝑖 and 𝑛𝑛 2𝑇𝑇 𝑖𝑖 are the path loss exponent of the corresponding slope. 𝑋𝑋 1𝑇𝑇 𝑖𝑖 and 𝑋𝑋 2𝑇𝑇 𝑖𝑖 are lognormal constants corresponding to the received power variations throughout the first and the second part of each track modeled by one slope.

  3, where the different propagation models are assessed. 𝑎𝑎 𝑇𝑇 𝑖𝑖 . 𝑙𝑙𝑙𝑙𝑎𝑎 10 (𝑑𝑑)2 -𝑏𝑏 𝑇𝑇 𝑖𝑖 . 𝑙𝑙𝑙𝑙𝑎𝑎 10 (𝑑𝑑) -𝑐𝑐 𝑇𝑇 𝑖𝑖 𝑑𝑑 > 3𝜆𝜆 (4.17)As previously defined, 𝑃𝑃 𝑟𝑟 (𝑑𝑑) is the received power in dBm at the distance d in meters, 𝑃𝑃 0 is the received power at 1 meter in dBm. 𝑃𝑃 0 is the received power at 3𝜆𝜆 in dBm. i is the track index, 𝑋𝑋 𝑇𝑇 𝑖𝑖 is a lognormal variable for the received power error throughout the first part of each track (Figure4.1) modeled by the one slope variation. n T i is the path loss exponent corresponding to the first part of the path. , b T i and c T i are constant parameters of the second order polynomial model that corresponds to the second part, i is the track (Figure4.1). 𝑃𝑃 0 ′ is the received power at 3𝜆𝜆. They are determined by forming a squared matrix as follows: � 𝑙𝑙𝑙𝑙𝑎𝑎 10 (𝑑𝑑 1 ) 2 𝑙𝑙𝑙𝑙𝑎𝑎 10 (𝑑𝑑 1 ) 1 𝑙𝑙𝑙𝑙𝑎𝑎 10 (𝑑𝑑 2 ) 2 𝑙𝑙𝑙𝑙𝑎𝑎 10 (𝑑𝑑 2 ) 1 𝑙𝑙𝑙𝑙𝑎𝑎 10 (𝑑𝑑 3 ) 2 𝑙𝑙𝑙𝑙𝑎𝑎 10 (𝑑𝑑 3 ) 1 𝑙𝑙𝑙𝑙𝑎𝑎 10 (𝑑𝑑 1 ) 2 𝑙𝑙𝑙𝑙𝑎𝑎 10 (𝑑𝑑 1 ) 1 𝑙𝑙𝑙𝑙𝑎𝑎 10 (𝑑𝑑 2 ) 2 𝑙𝑙𝑙𝑙𝑎𝑎 10 (𝑑𝑑 2 ) 1 𝑙𝑙𝑙𝑙𝑎𝑎 10 (𝑑𝑑 3 ) 2 𝑙𝑙𝑙𝑙𝑎𝑎 10 (𝑑𝑑 3 ) 1

		𝑛𝑛 𝑇𝑇 𝑖𝑖 =	𝑃𝑃 𝑟𝑟 (𝑑𝑑) -𝑃𝑃 0 -𝑋𝑋 𝑇𝑇 𝑖𝑖 -10𝑙𝑙𝑙𝑙𝑎𝑎 10 (𝑑𝑑)	(4.18)
		a T i � �	𝑎𝑎 T i 𝑏𝑏 T i 𝑐𝑐 T i	� = � 𝑃𝑃 𝑟𝑟 (𝑑𝑑 3 ) -𝑃𝑃 0 ′ 𝑃𝑃 𝑟𝑟 (𝑑𝑑 1 ) -𝑃𝑃 0 ′ 𝑃𝑃 𝑟𝑟 (𝑑𝑑 2 ) -𝑃𝑃 0 ′	�	(4.19)
		Where 𝑑𝑑 1 , 𝑑𝑑 2 , and 𝑑𝑑 3 are three different real tag-reader distances over the
	corresponding track.
		Let
		𝐴𝐴 = �			�	(4.20)
		And
	by:	The Dual One Slope with Second Order Polynomial Model (DOSSOM) is hence given 𝐵𝐵 = � ′ 𝑃𝑃 𝑟𝑟 (𝑑𝑑 1 ) -𝑃𝑃 0 𝑃𝑃 𝑟𝑟 (𝑑𝑑 3 ) -𝑃𝑃 0 ′ 𝑃𝑃 𝑟𝑟 (𝑑𝑑 2 ) -𝑃𝑃 0 ′ � (4.21)
	𝑃𝑃 𝑟𝑟 (𝑑𝑑) = � The solution of equation (4.19) determines the attenuation parameters of each track as 𝑑𝑑 ≤ 3𝜆𝜆 𝑃𝑃 0 -10. 𝑛𝑛 𝑇𝑇 𝑖𝑖 . 𝑙𝑙𝑙𝑙𝑎𝑎 10 (𝑑𝑑)+𝑋𝑋 𝑇𝑇 𝑖𝑖 𝑃𝑃 0 follows: 𝑎𝑎 𝑇𝑇 𝑖𝑖 ′ -Thus, the attenuation parameter of the first part of each track 𝑇𝑇 𝑇𝑇 is determined as � 𝑐𝑐 𝑇𝑇 𝑖𝑖 𝑏𝑏 𝑇𝑇 𝑖𝑖 � = 𝐴𝐴 -1 𝐵𝐵 (4.22)
	follows:

  With i indicates the track. 𝑛𝑛 𝑤𝑤1 and 𝑛𝑛 𝑤𝑤2 are the WAAF corresponding to the first and the second slope of the propagation model, respectively. 𝑛𝑛 1𝑇𝑇 𝑖𝑖 and 𝑛𝑛 2𝑇𝑇 𝑖𝑖 are the slopes associated to each track. 𝑁𝑁′ 𝑇𝑇𝑃𝑃𝑇𝑇𝑇𝑇𝑇𝑇 and 𝑁𝑁′′ 𝑇𝑇𝑃𝑃𝑇𝑇𝑇𝑇𝑇𝑇 represent the respective total number of positions over the With i indicates the track. 𝑛𝑛 𝑤𝑤1 is the weighted average attenuation factor corresponding to the first slope of the propagation model. 𝑛𝑛 𝑇𝑇 𝑖𝑖 is the attenuation factor relative to each track. 𝑁𝑁′ 𝑇𝑇𝑃𝑃𝑇𝑇𝑇𝑇𝑇𝑇 represents the total number of positions over the first segment and 𝑁𝑁′𝑃𝑃𝑃𝑃𝑃𝑃

		7 𝑇𝑇=1	𝑁𝑁′𝑃𝑃𝑃𝑃𝑃𝑃 � 𝑇𝑇 𝑖𝑖 𝑁𝑁′ 𝑇𝑇𝑃𝑃𝑇𝑇𝑇𝑇𝑇𝑇	(4.28)
	While d > 3λ,
		𝑛𝑛 𝑤𝑤2 = � 𝑛𝑛 2𝑇𝑇 𝑖𝑖 7 𝑇𝑇=1	𝑁𝑁′′𝑃𝑃𝑃𝑃𝑃𝑃 � 𝑇𝑇 𝑖𝑖 𝑁𝑁′′ 𝑇𝑇𝑃𝑃𝑇𝑇𝑇𝑇𝑇𝑇	(4.29)
	two segments. N′ Pos � and N′′ Pos T i � are the respective number of positions over each track of T i
	the corresponding slope.
	Furthermore, applying WAAF with DOSSOM, equation (4.17) becomes:
	𝑃𝑃 𝑟𝑟 (𝑑𝑑) = �	𝑃𝑃 0 -10. 𝑛𝑛 𝑤𝑤 1 . 𝑙𝑙𝑙𝑙𝑎𝑎 10 (𝑑𝑑)+𝑋𝑋 𝑤𝑤 1 𝑃𝑃 0 ′ -𝐴𝐴 𝑤𝑤 2 . 𝑙𝑙𝑙𝑙𝑎𝑎 10 (𝑑𝑑) 2 -𝐵𝐵 𝑤𝑤 2 . 𝑙𝑙𝑙𝑙𝑎𝑎 10 (𝑑𝑑) -𝐶𝐶 𝑤𝑤 2 𝑑𝑑 > 3𝜆𝜆 𝑑𝑑 ≤ 3𝜆𝜆	(4.30)
	The attenuation parameters are then defined in the following equations:
	While d ≤ 3λ,
		𝑛𝑛 𝑤𝑤1 = � 𝑛𝑛 𝑇𝑇 𝑖𝑖 7 𝑇𝑇=1	𝑁𝑁′𝑃𝑃𝑃𝑃𝑃𝑃 � 𝑇𝑇 𝑖𝑖 𝑁𝑁′ 𝑇𝑇𝑃𝑃𝑇𝑇𝑇𝑇𝑇𝑇	(4.31)
				𝑇𝑇 𝑖𝑖 �	the
	respective number of positions over each track.
	While d > 3λ,

  With i indicates the track. 𝑎𝑎 𝑇𝑇 𝑖𝑖 , 𝑏𝑏 𝑇𝑇 𝑖𝑖 and 𝑐𝑐 𝑇𝑇 𝑖𝑖 are the constants associated to each track.

	(4.32)
	N′′ Pos � T i
	N′′ Total
	They are given by equation (4.22) in sub-subsection 4.5.2.
	𝑇𝑇 𝑖𝑖 �

𝑁𝑁" 𝑇𝑇𝑃𝑃𝑇𝑇𝑇𝑇𝑇𝑇 represents the total number of positions over the second segment and 𝑁𝑁"𝑃𝑃𝑃𝑃𝑃𝑃

Table 4 .

 4 2 Comparison of the Estimated Distance Errors via AF90 and WAAF Attenuation

			Factors
	Tracks	Tag-Reader Distance [m]	Estimated Distance Errors [m] AF90 WAAF
	A60	2.8 3.5	0.71 0.83	0.58 0.72
	A90	2.8 3.5	0.63 0.68	0.54 0.63
	A120	2.8 3.5	0.83 0.75	0.41 0.48
	It can be noticed that the suggested WAAF calibration approach improves the estimated

distance errors compared to the conventional one, that is based on the attenuation parameters of the central track (A90) only.

Table 5 .

 5 1 The mean estimated distance errors for different radii of constellations with OSM

	Constellation's Radius	𝜆𝜆/8	𝜆𝜆/4	𝜆𝜆/2	3𝜆𝜆/4	𝜆𝜆	5𝜆𝜆/4	3𝜆𝜆/2	7𝜆𝜆/4
		R [cm]	8.75	17.5	35	52.5	70	87.5	105	122.5
		Mean
		Distance	2.24 2.09 2.31 2.05	1.94	1.98	2.13	2.2
	Track	Error [m]
	A90
		Standard deviation	1.58 1.03 1.43 1.31	1.26	1.32	1.36	1.41

Table 5 .

 5 2 The mean estimated distance errors for different constellations with OSM

	Constellation Tags' Number	3 Tags 4 Tags 5 Tags 5 Tags (Penta)
	Track	Mean Distance Error [m]	2.04	1.94	1.92	1.95
	A90	Standard deviation	1.07	1.26	1.05	1.31

Table 5 .

 5 3 Mean estimated distance errors based on real measurements

	Single Tag Constellation of Tags

Table 5 .

 5 4 The mean estimated distance errors by simulation

Single Tag Constellation of Tags

	Mean Distance Error [m]	1.02	0.81
	Track A60
	Standard deviation	0.59	0.45
	Mean Distance Error [m]	2.41	1.93
	Track A90
	Standard deviation	1.62	1.21
	Mean Distance Error [m]	1.19	0.71
	Track A120
	Standard deviation	1.04	0.49

Table C .

 C 1 Comparative summary for different RFID-based ILSAccording to TableC. 1, the presented localization systems provide several solutions for indoor positioning. Authors focused on the location accuracy while neglecting the complexity and the cost of the number of deployed UHF RFID tags. More precisely,[1],[2] and[3] use around 0.66, 0.56 and 0.88 active RFID tags per square meter while performing LANDMARC and VIRE algorithm. The solution of[4] uses 1.36 active RFID tags per square meter to improve the location accuracy. After providing a fair number of use case examples, we can notice that most systems count on a large number of deployed active RFID tags for better performance.

	RFID based ILS	Accuracy [meters]	Number of Active Tags deployed	Technique	Tag density [ 𝒎𝒎 𝟐𝟐 ]
	[1] L. M. Ni	2	24	LANDMARC	0.66
	[2] D. Cui	0.75	28	LANDMARC	0.56
	[3] E. Ferraz	0.37	8	VIRE	0.88
	[4] Z. Dian	0.45	49	COCKTAIL	1.36
	Proposed	Single Tags	0.9	4	Multilateration	0.062
	System	Constellations	0.6	16	Multilateration	0.25

Acknowledgments

This work was carried out jointly at the University Paris Est, in the laboratory of Electronique, Systèmes de Communication et Microsystèmes (ESYCOM), directed by Prof. Jean-Marc Laheurte, and the Lebanese University, in the laboratory of Systèmes Electroniques, Télécommunications et Réseaux (LaSTRe), directed by Prof. Bachar El Hassan. It was funded

Availability

The technology is readily available and no need for any end-user hardware.

Active RFID Tags

An active RFID tag is equipped with a transceiver and an internal battery. This active tag provides an autonomous signals transmission to the RFID reader antenna. The embedded battery makes active transmitters heavier and costlier but allows for long detection range up to 200 meters outdoors [1].

Figure 1.12 Typical architecture of Active RFID systems

The active RFID technology can be used in localization thanks to its long range detection. For instance, [START_REF] Seco | Autocalibration of a wireless positioning network with a FastSLAM algorithm[END_REF] demonstrates an auto-calibration method using 64 active RFID tags operating at 433 MHz, in a 1600 square meters indoor environment; The RFID tag detection range can reach 20 meters. The detected position accuracy is 4.9 meters using the RSS metric and 3.4 meters while applying a particle filter.

Based on the associated high coverage, we are interested to implement a new ILS using active UHF RFID tags. The proposed system will be described in the following chapter.

Summary for Optimal ILSs Performance

Several recently implemented ILSs were studied along with their advantages and limitations. Some of these ILSs meet high reliability, strict energy efficiency and robustness indoors. The choice of technology is a key criterion to achieve the most suitable indoor localization solution. Table 1.3 summarizes the optimal ILSs' performance for each technology, based on the associated algorithms and accuracy.

Summary

In this chapter, we have detailed the most used positioning metrics (ToA, TDoA, AoA and RSS) followed by the different localization techniques (proximity, triangulation, trilateration and fingerprinting). In addition, different technologies, especially those based on the radio frequency, (ZigBee, Bluetooth, WiFi, UWB and RFID) are defined taking into account their features and limitations in indoor localization such as availability, cost, reception range, latency, location accuracy and scalability. The choice of the suitable technology depends on the user's application and its requirements. Based on the literature reviews, with particular emphasis on some of the IPSs performances', these competitive RF technologies are then evaluated and compared.

Based on its features, a special focus was given to the RFID technology. It will be implemented within our system. More details will be presented in the following chapters.

Multilateration

As defined in chapter 1, section 1.3.3, multilateration is the process that estimates a target location based on the estimation of distances from several reference points [34]. Within the proposed RFID based localization system, the reader position is determined based on multi tags-reader distances estimation and using RSS values collected by the RFID reader. However, indoor, RSSs are inversely proportional to the tag-reader distance and the random noise factor [35]. Each estimated distance is represented by a circle around the fixed associated tag. The intersection of the different circles provides a common point or a coverage area of the received signals as shown in Figure 2.12.

Figure 2.12 Positioning by Multilateration

According to the Euclidean distance formula, nonlinear equations are formulated as follows:

(𝑎𝑎 -𝑚𝑚 𝑇𝑇 ) 2 + (𝑏𝑏 -𝑦𝑦 𝑇𝑇 ) 2 + (𝑐𝑐 -𝑧𝑧 𝑇𝑇 ) 2 = 𝑑𝑑 𝑇𝑇 2 (2.7) P(a,b,c) represents the unknown reader's location. (𝑚𝑚 𝑇𝑇 ,𝑦𝑦 𝑇𝑇 , 𝑧𝑧 𝑇𝑇 ) represents the 𝑖𝑖 𝑇𝑇ℎ known tag coordinates, and 𝑑𝑑 𝑇𝑇 represents the distance between the reader and the 𝑖𝑖 𝑇𝑇ℎ tag.

The multilateration technique is based on distances to N reference RFID tags (N>3), assumed fixed in space and located at known coordinates (𝑚𝑚 𝑇𝑇 ,𝑦𝑦 𝑇𝑇 , 𝑧𝑧 𝑇𝑇 ). A movable RFID reader is located at an unknown position P(a,b,c). The high number of tags may improve the Ela Manufacturer provides Figure 2.15 that can be used to deduce the relationship between the RSS values collected by the reader and the received power in dBm. The received power in dBm is expressed as follows:

𝑃𝑃 𝑑𝑑𝑑𝑑𝑑𝑑 = 30.84 -0.632 * 𝑅𝑅𝑅𝑅𝑅𝑅 (2.15) Where 𝑃𝑃 𝑑𝑑𝑑𝑑𝑑𝑑 is the received power in dBm. RSSI is the received signal strength in decimal.

Figure 2.15 RSSI vs Power [dBm]

It can be noticed, on Figure 2.15, that RSS values vary linearly in the range between 128 to 204. In our further processing, RSS values out of this linear range are filtered out.

Besides, it is worth mentioning that the step between two adjacent RSS values is equivalent to 0.6 dB. Thus, the quantization error, based on the RFID reader dynamic range, is equivalent to ± 0.3 dB.

Summary

In this chapter, the major drawbacks of RFID-based ILSs and associated solutions are presented. A conventional system is then described. It is divided into two stages: offline and online. The offline stage is focused on building a radio map for the indoor environment.

Whereas, the RFID reader position is determined during the online stage. Both, offline radio map and online distances estimation are done using the average technique for RSSs combining.

The simplest and most used indoor propagation model (OSM) followed by the multilateration technique are also shown. Finally, the RFID equipment is described.

Figure 3.1 Indoor site of experiments in the classroom

The hall is an indoor area of 205 square meter, with a traditional ceiling, as shown in Figure 3.2. It is also complex for RF signals propagation because of the geometrical shape (shape of L), and the presence of some metallic objects such as big heaters, billboard, fire detectors and doors. Then, materials constituting the environment and subdivisions such as: walls, doors, windows, and others are characterized by their electrical properties (transmission, reflection, scattering and diffraction coefficients).

WinProp offers an extensive library where properties are defined for different frequency bands and materials. Table 3.2 states the empirical transmission and reflection losses corresponding to each material at 433 MHz. In addition to transmission and reflection, diffraction and scattering are also taken into consideration.

Diffraction describes the propagation variation between illuminated and shadowed regions when there are edges or wedges that participate to the diffraction process. It is important when edges and wedges dimensions are closed to a factor of the wavelength. We are working in the UHF frequency band; diffraction is significant. Two types of diffractions are considered:

the Geometric Theory of Diffraction (GTD) [8] and the Uniform Theory of Diffraction (UTD)

[9]- [11]. Empirical formulas are also included when applying empirical propagation models.

Scattering is directly related to the roughness and finite surfaces with small dimensions compared to the wavelength [12]. In our case, several surfaces are included in the indoor simulated model such as those of the heater and the LCD projector. Scattering will have a direct effect by reducing the reflected energy in the expected direction [13].

After defining all Fresnel parameters and empirical losses coefficients for the materials that constitute the considered indoor environments, four views (x-z, y-z, x-y and 3D view) appear on the main panel, as illustrated in Figure 3.6 and Figure 3.7, respectively. Finally, the two configurations, containing the vectors database, will be created and saved under the extension *.dbi.

Consider that the transmitter Tx is fixed in a specific position, as illustrated in Figure 3.15. The indoor environment description is used to determine whether corners are concave or convex.

Figure 3.15 Scenario with transmitter Tx, receiver Rx and different types of corners [7]

To reach the receiver Rx, the Dominant Path model's (DPM) algorithm is focused on tracking the best path for the signal, as presented in Figure 3.16. The tree structure starts with corners visible from the transmitter Tx. Each time the receiver Rx is found in the tree, corners along the path can be determined by following the branches back to the transmitter Tx. More precisely, DPM considers only the most relevant path that contributes to the dominant path.

According to [17], it is applied in an office building. One can deduce that not all rays and interactions, between the transmitter and receiver, are explored. Only the dominant path is used; this will lead to a reduced computational complexity at the expense of accuracy, especially in NLoS scenarios.

Simulations versus Measurements

In this section, modeling, using an active RFID tag, is analyzed and compared to the

Tests in the Classroom

In the classroom environment, the received power values are collected every 50 centimeters starting from the front wall, at sixty-four reference positions covering the whole classroom environment, during the offline stage. These positions are distributed over seven tracks A30 till A150 to characterize the signal behavior in terms of the (tag-reader) distance, as illustrated in Figure 3.22. The space separating two successive positions is constant and different than that considered within the online stage.

The OSM, already described in subsection 2.4.3, is applied to determine the estimated distance. The RFID reader was moved forward to collect RSS samples at forty-one positions over A90 and seventy-six positions over A'90. The collected received power values are analyzed and compared with those obtained by simulation over the same tracks. More precisely, the difference between the real and simulated power values as well as the standard deviation are calculated. To assess the accuracy of the simulated hall configuration, it is necessary to achieve a difference, between the received power values and the simulated ones, less than 0.1 dB and not exceeding 0.5 dB. Beyond this limit, the simulation will lose its precision. Some major adjustments on the environment modeling are recommended in order to improve the received power matching. Weighted Average Attenuation Factor (WAAF). This approach is proposed to improve the system reliability by covering the whole indoor environment.

It is worth recalling that our positioning system is divided into offline and online stages:

the offline involves an environmental calibration phase. It is needed to determine the propagation channel attenuation parameters. During the online stage, the RFID reader position It is worth to recall that the tags' transmission is intermittent. As mentioned in section 2.5, 40 seconds are needed to collect the 200 samples, in the offline stage. Hence, the propagation channel, between the tag and the reader, can be considered as non-stationary. This channel variance may induce RSS variability.

In addition, the RFID reader quantization error is equivalent to ± 0.3 dB [9]. Hence, the standard deviation values are divided into three categories: the first one is where the standard deviation is less than the quantization noise. The second category illustrates the deviation values between 0.3 and 0.5 dB. Greater than this limit, another category should be considered, in order to identify the critical signals propagation areas.

In this context, it can be noticed that RSS acquisitions fluctuate widely when the RF signals are strongly affected by the indoor environment. More precisely, the largest standard deviations, obtained over tracks A30 and A45, are mainly induced by the corner in front of these two tracks.

Concerning track A90, the maximal standard deviation is less than that of other tracks because most of the emitted signals are far from walls, heater, and pillar, then, signals propagation is nearly LoS over this track.

However, we observe that signals are strongly affected over tracks A120, A135 and A150. Hence, it is necessary to compare the RSS distributions over these three tracks with that of one of the least affected tracks. measurements over track A60 are clustered and span a small range between 139 and 140 that correspond to a received power of -56.94 and -57.58 dBm [9], respectively. In this case, it could still be reasonable to apply the averaging technique to combine the RSS acquisitions.

RSS observations are combined via MLE in both offline and online stages.

RSS values combined by MLE in offline and online stages

The values 𝑅𝑅𝑅𝑅𝑅𝑅 � within the conventional system and 𝑅𝑅𝑅𝑅𝑅𝑅 𝑀𝑀𝑀𝑀𝑀𝑀 within the proposed solution are converted to power values in dBm.

The comparison, between the performance of averaging and the MLE combining techniques, will be elaborated, through indoor localization assessment, in section 4.8.

Empirical Indoor Propagation Models

COST 231 Model

COST 231 is a One Slope Model (OSM) described in [6] and already presented in chapter 2, subsection 2.4.3. The received power expression is given by:

Where 𝑃𝑃 𝑟𝑟 (𝑑𝑑) is the received power in dBm at a distance d in meters, 𝑃𝑃 0 is the received power at the distance 𝑑𝑑 0 in dBm and n is the path loss exponent. The term 𝑋𝑋 describes the standard deviation of the received power values throughout the corresponding area.

WINNER II D112 V1.2 Model

Referring to [7], the WINNER II channel model was proposed for indoor, indoor to outdoor, outdoor to indoor and outdoor scenarios. Initially, it was applied at 2 and 5 GHz. Then, it was extended over the frequency range 2 to 6 GHz. Two models were proposed; the first is for LoS (Without any obstacles). The path loss is then calculated following equation (4.7):

Where 𝑓𝑓 𝑐𝑐 is the frequency in GHz, d is the distance between the transmitter and the receiver in meters, 𝑛𝑛 𝑊𝑊 is number of walls and 𝑛𝑛 𝑓𝑓 is number of floors.

Attenuation Factor Classic Model

The Attenuation Factor Classic Model (AFC) model is an empirical indoor office path loss model. [14] presents the path loss measurements conducted in a modern office building using a 5. As defined previously, 𝑃𝑃𝐿𝐿(𝑑𝑑) is the path loss in dB, d is the distance in meters and n is the path loss exponent.

Second Order Polynomial Propagation Model

The Second Order Polynomial Propagation Model (SOPPM) seems to be the best way

Proposed Empirical Indoor Propagation Models

Empirical investigation has been a common methodology to study radio waves propagation and path loss modeling. Our approach follows this practice. A series of measurements were conducted in the classroom environment in order to determine the propagation channel attenuation coefficients. The received power is then depicted and matched with different propagation models in a way to justify the use of a two slopes model.

Dual One Slope Propagation Model

According to recent experiments presented in [16], the received power decays sharply when the measurement range is up to 1.5 meters. Then, the signal starts to decay slowly. In our

Distance Estimation

After determining the propagation model parameters, tags-reader distances are estimated during the online phase. The averaged RSS acquisitions are further converted to power values in dBm.

Knowing the power captured by the reader and following equation (4.16) (DOSM model), the distance can be estimated as:

Whereas, by applying DOSSOM (equation 4.17 Finally, the distance error 𝜖𝜖 𝑑𝑑 can be calculated as follows:

where 𝑑𝑑 𝑟𝑟 is the real tag-reader distance.

Distance Error Analysis

In this section, the Mean Distance Errors (MDE) obtained with the different indoor propagation models are studied and analyzed. As already stated, this analysis is assessed during the offline stage in a way to validate the performance of the proposed propagation models (DOSM and DOSSOM). Table 4.1 shows the variation of the MDEs and the 90% Cumulative Density Functions (CDFs) of the distance errors along the three different tracks A60, A90 and A120 respectively. The optimal MDE, obtained with the existing propagation models, is 67 centimeters over track A60. However, the optimal MDEs obtained, over the same track, with DOSM and DOSSOM are 56 and 51 centimeters, respectively. Hence, it can be noticed that the proposed indoor propagation models DOSM and DOSSOM improve the distance estimation by 79.18 percent compared to the distance errors obtained with the COST 231 model over track A90.

Moreover, these proposed models enhance the distance estimation by 23.88 percent compared to results obtained with the best performing model SOPPM that matches perfectly with the received power. Thus, DOSM and DOSSOM are effective propagation models that can improve the accuracy in the proposed localization system.

Weighted Average Attenuation Factors

In the literature, several studies evaluate the impact of the environment calibration on localization systems accuracy [17]- [22]. However, most of these studies have focused on signals propagation characteristics only [17] and [18]. Work in [17] creates a Ray Launching based simulation model to evaluate an indoor localization system implemented in an office.

Positions errors highly depend on the number of considered Bluetooth Low Energy (BLE) devices used to extract the accurate signal attenuation parameters. In the same context, [18] focuses on the calibration of Wi-Fi-based indoor tracking systems to be used by smartphones.

Experiment was conducted in a multi-room office laboratory. To build an accurate signal propagation model corresponding to the indoor environment, a novel in-motion calibration methodology is introduced. This calibration uses three different propagation models based on RSS collection, supplemented by a particle filter. According to the presented results, using the in-motion calibration mechanism considerably improves the tracking accuracy. In these studies [17] and [18], some important challenges, such as the fluctuation of RSS measurements and the long durations as well as manpower needed for data collection are not addressed.

Beyond this, some studies focused on avoiding time-consumption for data collection.

For instance, [19] develops a new interpolation algorithm to optimize the number of pre-chosen calibrated points to build a radio map. In addition, work in [20] proposes a novel Simultaneous Calibration and Localization (SCAL) algorithm to improve real-time positioning and calibrating accuracy in indoor scenarios. The proposed framework is divided into the Target Localization and Beacon Calibration (TLBC) section and the Global Optimization (GO) section. Positioning accuracy is improved to reach 0.24 meters. Despite the improvement achieved by the proposed solutions [19] and [20], issues like the computational complexity and the system's cost were not addressed.

Looking at beamforming-based localization approaches, work in [21] applies an adaptive beamforming AoA estimation technique based on the Minimum Variance Distortion less Response (MVDR) algorithm. This technique reaches a sub metric positioning accuracy.

In the same context, [22] presents an Ultra-Wide Band Impulse Radio (UWB-IR) beamforming array targeted for a precise directional positioning and tracking of moving objects in complex indoor environments. This beamforming transmitting array is able to achieve full scanning range from -90 to +90 degrees with a resolution of 5 degrees. Despite the high accuracy achieved in both studies, the use of beamforming for angles estimation requires a complex enduser hardware, which increases the system's cost and complexity.

In our case, to trade-off between the system's performance, complexity and hence cost, we propose an RSS calibration method via the Weighted Average Attenuation Factor (WAAF).

It aims to increase the accuracy of the localization system through a better propagation channel model. Compared to the attenuation factor AF90 of our conventional localization system, already introduced in chapter 2, subsection 2.4.4, WAAF approach takes into account all the positions over all tracks, in order to extract accurate attenuation parameters corresponding to the considered environment (Figure 4.1).

In fact, our conventional indoor localization system uses only the calibration parameters of the track A90, that is characterized by the highest number of positions. These attenuation parameters are abbreviated as AF90. The attenuation parameters are then defined in the following equations:

position is then estimated by applying either the defined propagation model DOSM or DOSSOM, followed by multilateration.

The DOSM propagation model with WAAF is already presented in equation (4.27).

Moreover, the DOSSOM propagation with WAAF is also previously expressed in the equation The position error is estimated at thirty-two different positions. Among these positions, twenty-four are uniformly distributed in the space, with a distance of 70 centimeters ≅ 𝜆𝜆, over the three tracks A60, A90 and A120. This step differs from the one used within the calibration stage (50 centimeters) in order to evaluate our system's accuracy on tracks.

Eight particular positions are chosen out of the considered tracks, to assess the effects of the classroom geometry and structure as well as those produced by the metallic elements distributed within. P1 and P7 were selected to check the impact of the LCD projector on the positioning error. P3 and P8 reflect the influence of the pillar on the left side of the classroom.

P5 and P2 assess the heater's effect. Finally, P4 and P6 are between tracks A60 and A90, on the right side of the classroom, where the impact of the metallic objects is less compared to other areas.

Twenty samples of the RSS are acquired at each position; they are combined by the conventional averaging as well as by the Maximum Likelihood Estimator (MLE), in both the calibration and the positioning stages.

Moreover, tags-to-reader distances were estimated using one of the propagation models DOSM and DOSSOM, considering the two calibrations approaches: that with the basic attenuation parameters of the central track A90 (AF90) and that with the Weighted Average Attenuation Factor (WAAF) defined in the section 4.6.

Then, by applying the multilateration technique, the position of the RFID reader is estimated.

Positions errors', obtained by performing the MLE in both stages, were determined and compared to those obtained while combining the RSS samples by averaging.

Location assessment via AF90 vs WAAF

The following shows the location accuracy, using the RSS averaging technique within the offline and online stages, in order to evaluate the positioning improvement thanks to the proposed WAAF calibration approach. Overall, the suggested indoor localization architecture improves the position accuracy by up to 60 percent hence reducing the position error from 2.2 meters to 90 centimeters.

Summary

The main challenge of the proposed localization system is to mitigate locations error.

The MLE is introduced as a combining technique in order to improve the calibration phase's reliability and the localization phase's accuracy. It is applied to combine the RSS samples collected at each position in both offline and online stages. It improves the received signals'

stability compared to the typical method, i.e. averaging.

Moreover, two new empirical indoor localization models were proposed. They were compared with those found in the literature, using real measurements in a classroom environment of 63.75 square meters. Experimental results confirm that two propagation models DOSM and DOSSOM present an improvement in estimated distances and positions error.

In addition, the presented WAAF propagation channel modeling shows closest agreement of the predicted path loss in the indoor environment. This approach gives a reliable calibration that considers all multipath parameters. Concerning the proposed system's accuracy, it was validated during the positioning phase, by applying the proposed propagation models (DOSM or DOSSOM) followed by the multilateration technique and using four active RFID tags only.

Quantitatively, the location error of the suggested system is 90 centimeters, with a cumulative density function at 90%, while deploying 0.062 RFID tags per square meters only. This accuracy will be optimized more, by introducing the constellation approach in the following chapter.

Optimal Constellation's Radius

In According to the MDE values presented in Table 5.4, the constellation performs better than single tags thanks to signals diversity. For instance, within simulations, the constellation of tags decreases the distances errors to 21 centimeters over the track A60, 48 centimeters over A90 and 42 centimeters over A120. In addition, the constellation of tags presents more stability due to the receiving signals' diversity created by the group of RFID tags. In fact, the obtained standard deviation is smaller than that for the single tag system in the indoor environment.

Furthermore, referring to Table 5.3 and Table 5.4, it can be noticed that measurements and simulations present very closed results. More precisely, these tables show that the maximum difference in MDEs obtained between measurements and simulations is only 4 centimeters over A60, and 1 centimeter over tracks A90 and A120 in the single tag scenario.

Similarly, this difference reaches only 1 centimeter over A60, 5 centimeters over A90 and 2 centimeters over A120 in the constellation of tags scenario.

Localization Assessment

This subsection aims to assess the localization performance of the proposed system, comparing it to the conventional system under different aspects. Experiment was performed in the unfurnished classroom environment that has been presented in chapter 3, section 3.2.

Restate also, as described in chapter 2, section 2.4, the signal propagation within the offline stage is characterized over seven paths A30 to A150, using one RFID tag placed on the center of the front wall, as shown in Figure 5.3. Two hundred RSS values were collected every 50 centimeters over the seven trajectories.

First, the RSS samples are combined via the averaging technique. Then, MLE is applied in order to increase the location accuracy and improve the system's reliability. The propagation models DOSM or DOSSOM, with WAAF already introduced in chapter 4, were applied over each track to determine reliable attenuation coefficients, corresponding to the classroom environment.

Within the online stage, the localization phase is performed in a way similar to the process described in chapter 4, section 4.

Localization Assessment based on Constellations

In this subsection, location accuracy is analyzed while RSS samples are combined by averaging.

After performing the WAAF calibration procedure that was introduced in the previous chapter, positions errors with the constellation of tags are analyzed and compared with those obtained using single tags. Considering the gathered results, localization based on the constellation approach, while combining RSS samples by averaging, improves location accuracy by 46.7 percent as compared to that obtained with the single tags architecture. Thus, the efficiency of using the constellations of tags for localization purposes in an indoor environment is well proved. Recall that, in the single tag scenario, the proposed RFID based localization achieves optimal accuracy of 1.3 meters and 90 centimeters, while combining RSS samples by applying the averaging technique and the Maximum Likelihood Estimator, respectively. However, the constellation scenario's location accuracy reaches 80 and 60 centimeters, while using the averaging technique and the Maximum Likelihood Estimator, associated with WAAF respectively. Thus, the location accuracy is improved by 60 percent while performing the constellation of tags within the proposed system based on MLE and WAAF.

Summary

A new approach for a positioning system based on constellations of RFID tags is presented and studied in order to increase the localization accuracy. It consists in replacing the single tag by a group of tags and follows the same concept as that of MISO communication systems.

Various radii and different numbers of tags per constellation are studied in-depth.

Moreover, mean estimated distance errors, using the average value of the power received by the RFID reader and emitted by the constellation, are widely elaborated. Based on measurements and simulations, the optimal constellation is constituted of four RFID tags and has a radius equal to the wavelength. In terms of the distance error, the constellation performance is compared to those obtained with the single tag scenario.

Global Evaluation of Performance and Deployment

Density

The main aim of the thesis was to develop and implement an accurate and reliable ILS based on the active UHF-RFID technology. It shall also provide the high locations accuracy with a reduced number of RFID tags deployed.

The concept of our localization system is classified as a moving RFID reader and fixed active tags. It is divided into two stages: offline and online. The offline stage represents the environment calibration and the online one is properly the positioning phase.

Performance of most RFID based indoor localization systems depend mainly on the number of deployed RFID tags or readers and on advanced positioning algorithms. Our attention is drawn to the number of deployed RFID tags and the location accuracy achieved with active UHF RFID positioning systems already presented in chapter 2, section 2.2, as compared to our proposed system features.

Associated to the installed software, different indoor location methods such as LANDMARC, VIRE and others have been introduced [1]- [3]. LANDMARC is the first feasible technique using active RFID tags as anchors, usually placed in the form of a regular grid with limited number of RFID readers. For instance, [1] introduces the LANDMARC concept as a solution while 4 readers and 24 RFID tags operating at 308 MHz, are deployed in an indoor environment of 36 square meters. Recently, [2] establishes an RFID based localization system using 4 readers and 28 reference tags, operating at 433 MHz. The improved LANDMARC approach presents an average estimated positioning error of 75 centimeters in 50 square meters. Therefore, VIRE method is applied to improve the localization performance based on LANDMARC. [3] adapts VIRE solution by adopting an array formed by 8 reference tags and one RFID reader. This approach is validated in 9 square meters through simulations.

The achieved average location error is 37 centimeters.

Otherwise, researchers focused also on developing algorithms to improve the localization system performance. For instance, [4] shows a novel hybrid system for indoor localization; both SA-LANDMARC and COCKTAIL algorithms were introduced within a tested area of 36 square meters using 49 RFID deployed tags, operating at 303.82 MHz. The accuracy reached 70 and 45 centimeters respectively. Despite the SA-LANDMARC's and 60 centimeters using only four and sixteen active RFID tags, respectively, in a classroom environment of 63.75 square meters.

Conclusions and Future Works Conclusions

Most ILSs have some common and critical issues such as the instability of RSS measurements affected by the multipath effects, the human error as well as the thorough cost of manpower/time for data collection.

In addition, none of the indoor propagation models found in the literature and used for calibration have yet successfully provided a reliable radio map covering all indoor environments.

In the same context, ILSs often need an accuracy of the sub-meter level and adaptation to multiple scenarios. Hence, there is no unique solution that can cover all challenges of reliability, simplicity, accuracy, etc.

To this end, our work conclusions are summarized in accordance with four objectives.

The first one was to model indoor environments in order to reduce time needed for real measurements. Then, based on empirical studies, the second objective was to suggest two generic and configurable indoor propagation models i.e. the Dual One Slope propagation Model (DOSM) and the Dual One slope with Second Order propagation Model (DOSSOM) followed by a reliable calibration procedure, the Weighted Average Attenuation Factor (WAAF), that covers the whole considered indoor environment. Moreover, in order to improve localization performance, while implementing a simple and low-cost system, two purposes were elaborated: introducing constellations of RFID tags instead of single tags, as well as combining RSS samples collected by the RFID reader via the Maximum Likelihood Estimator (MLE).

Indoor Environment Modeling

Series of experiments were conducted in a classroom to analyze the coverage of propagating signals indoors. These experiments need the collection of a large amount of data, which require manpower and is large time-consuming; all this complicate the creation of a reliable fingerprinting.

For this reason, the indoor environment is modeled, via the WinProp tool, in order to reduce the need for costly measurements.

Empirical losses and Fresnel parameters, corresponding to all materials that constitute the considered medium, were defined.

Signals propagation characteristics are then analyzed using 3D Ray-Tracing.

Experimental and simulated results had similar behavior with a maximal distance error difference of 10 centimeters (Figure 3.24), over the classroom environment.

Empirical Indoor Propagation Models and Reliable Calibration

Procedure

It is widely recognized that indoor propagation characteristics greatly differ from the outdoor ones, mostly because of the indoor environmental particularities, such as shorter tagsreader distances, power fluctuations, and obstacle effects. In the context of propagation modeling, several empirical indoor propagation models, found in the literature, tend to focus on a particular characteristic including temporal fading or inter-floors losses. However, none of these models have yet successfully created reliable attenuation parameters, totally covering the considered indoor environment.

The importance of the suggested models DOSM and DOSSOM reside in representing signals behavior better, while being generic and configurable.

Associated with the new calibration approach WAAF, DOSM and DOSSOM present higher reliability in characterizing signals attenuation by covering the entire classroom environment and using only one active RFID tag.

Constellation of Tags

In order to even more improve the location accuracy, the concept of constellation was introduced.

The constellation is a group of tags that operate at the same frequency. Using constellations can reduce the multipath effects by benefiting from signals diversity. After investigating performance of the constellation with different radii, shapes and number of tags, the diamond four-tags constellation) with radius equal to the wavelength presents the optimal accuracy.

Localization Performance

Once the attenuation parameters extracted accurately, multilateration is applied to estimate the reader location.

Most conventional ILSs combine RSS samples by averaging; the proposed system performs by applying the Maximum Likelihood Estimator (MLE) as combining technique during both stages.

The effectiveness of using MLE with WAAF is validated by reaching a location accuracy of 90 and 60 centimeters with the single and the constellation of RFID tags, respectively.

Finally, the analysis on the impact of tags density proposes a trade-off challenge between the optimized deployment and the location accuracy. Regarding our system's cost and complexity, only four active RFID tags are used in the single tag and sixteen in the constellation of tags scenario.

Future Works

Based on the aforementioned conclusions, some future research axis can be addressed to further improve the proposed localization system:

-The system resolution varies with the number of RSS samples collected by the RFID reader in both offline and online stages. Hence, the optimal number of power acquisitions should be well investigated.

-A theoretical study regarding the constellation of tags may be useful to confirm the optimal dimensions of the constellation of RFID tags already demonstrated empirically.

-Location accuracy provided by the proposed indoor localization system may be investigated in a three-dimensional plane in the same classroom environment and with the same number of deployed RFID tags.

-Experiments may be conducted in different and more complex indoor environments to fully justify the effectiveness of the empirical and configurable indoor propagation models (DOSM and DOSSOM) and validate the solution with the optimal constellation of RFID tags.

Publications

The work conducted during this thesis has led to the following publications: