A Azil

M Le Guern

K Touati

M Gomina

N Sebaibi

M Boutouil

F Streiff

S Goodhew

H Louahlia

CONSTRUCTION FIELD MONITORING OF A COB PROTOTYPE BUILDING

Keywords: Cob, monitoring, prototype building, density, compressive strength

Cob is an earthen building material made by soil, fibres and water used for millennia. However, cob construction disappeared out during the nineteenth century. These last years, it is experiencing a renaissance in Northwestern France and Southern England. Due to a limited technical knowledge, the investigation of engineering properties is important for modern design practice and code requirements. Moreover, to ensure building properties, it is necessary to have same quality mix along the building phases.

The aim of this study is to determine material variation during the monitoring of a cob prototype building in Normandy (France). This study investigated structural cob mix composition, water content, density, mechanical properties and thermal conductivity. Specimens shape used were cylindrical 110 x H220 mm and prismatic 300 x 300 x 70 mm.

Results indicated a variation in cob mix (water content, materials proportions) between three different lifts. These variations lead to different densities and, consequently, to variables compressive strengths: 0.99 to 1.38 MPa and thermal conductivities from 0.610 -0.816 W.m -1 •K -1 .

INTRODUCTION

Green construction is a major concern. In the building sector, a growing interest has been observed during the last decades within ecological materials. Like other earth construction techniques, cob has met with renewed interest for its low environmental impact [START_REF] Morel | Building houses with local materials, means to drastically reduce the environmental impact of construction[END_REF] and [START_REF] Ford | The Development of a Methodology for Creating an Earthen Building Inventory[END_REF].

European Buildings consume most of the energy, responsible for 40 % of final energy consumption [START_REF] Dixit | Identification of parameters for embodied energy measurement: a literature review[END_REF]. The use of local materials as well as the absence of hydraulic cement can reduces the building grey energy. The use of earth as a building material can contribute to the reduction of greenhouse gas emissions, thereby mitigating the risks associated with climate change. In addition, the sorption and evaporation capacity of the earth material allows for temperature harmonization and air filtering [START_REF] Röhlen | Construire en terre crue : construction, rénovation, finitions[END_REF]. There is limited literature on monitoring cob mix during construction of a real building. Knowing the material variability will allow to ensure building properties throughout the construction phase. This will help to satisfy modern design practices and code requirements. To the best of our knowledge, there is no normalized procedure to determine the mechanical performance of cob. Indeed, various specimen production protocols exist with differences in terms of size, mold, mix preparation, drying and water conditioning before testing [START_REF] Harries | The geological and geotechnical properties of earth material from central Devon in relation to its suitability for building in "Cob[END_REF][START_REF] Addison Greer | The effect of moisture content and composition on the compressive strength and rigidity of cob made from soil of the Breccia Measures near Teignmouth, Devon[END_REF][START_REF] Keefe | Earth Building -Methods and materials, repair and conservation[END_REF][START_REF] Pullen | Index and Engineering Properties of Oregon Cob[END_REF]Vinceslas et al., 2018). It must be noted that uniaxial compressive strength (UCS) and Young's modulus (E) of cob is most often carried out on double slenderness cylindrical specimens. The mechanical resistance of cob affects the wall height, which reduces construction time and cost (Hamard et al., 2016). The aim of this work is to monitor the material variation during the building of a cob prototype in Normandy (France). The study is based on the analysis of structural cob mix composition, water content, density, mechanical properties and thermal conductivity. Specimens used were cylindrical (110 x H220 mm) and Prismatic (300 x 300 x70 mm) shape. This scale 1 prototype building is part of the European Interreg project CobBauge (Fig. 1). The main objective of this project is to build a prototype complying with the French thermal regulation.

Fig. 1: outdoor and indoor view of the earth-based building

The earth, prototype building will have a total area of 20 m 2 for an indoor area of 13 m 2 . The soil-fibre walls will be built on a 70 cm high composite sub-base made of light concrete and thermopierre. This sub-base structure ensures the same thermal resistance of the soil-fibre walls. The earth, prototype building is also intended to establish the various constructive details to be used for this type of material. The ground plan of the building shows two different layers in the walls, structural cob indoor, and thermal cob outdoor with two different thicknesses used, 70 cm in the North and East for better thermal insulation and 50 cm in the South and West more exposed to solar radiation (Fig. 1 and2).

MATERIALS AND METHODS

Materials

The structural cob is made with a mix of two soils (soil 1 and soil 2) and flax straw. Soils were characterized according to their particle size and their clay activity. Particle size analysis was performed by mechanical sieving up to 80µm according to the standard XP P94-041 and by laser grain size for particles under 80µm following the standard ISO 13320. The two soils particle size distribution is presented in Fig. 3. Soil 1 has a high fine fraction (≈ 85 %). That fine fraction leads to the use of soil 2 as a granular corrector. The clay activity was determined according to blue value test with respect to the standard NF P94-068 and Atterberg's limit by following the standard NF P94-051 (Tab.1). Logically, soil 1 presents a higher clay activity than soil 2. The soils used were classified according to the LCPC-USCS classification.

Tab Mix and manufacturing specimens According to the amount of mix needed for the building prototype, a mechanical digger was used to accelerate the mixing process (Fig. 6). The theoretical formulation of cob mix is 1/3 (33 %) of soil 1, 2/3 (65 %) of soil 2 and 2 % of flaw straw fibre. The fibre content is relative to the dry soil mass.

Fig. 6: The cob mixes on the construction site

During the mixing process and building phase, cob mix samples were taken to measure the water content according to the standard NF EN ISO 17892-1, for all the building walls. Specimens were obtained by manual compaction rod. Three cylindrical specimens (110 x H220 mm) for each lift were used to determine compressive strength, apparent density (NF X31-501 Standard) and fibre content (Fig. 7). Three prismatic specimens (300 x 300 x 70 mm) for each lift were used to determine apparent density and thermal conductivity (Fig. 7). When the cylindrical and prismatic cob specimens were prepared, they were kept in 20 ± 2 °C within a relative humidity of 50 ± 5 % one day and dried at 40°C until reaching an equilibrium weight (three consecutive weighing's at 24 hours intervals within 1 % standard deviation).

Fig. 7: Cylindrical and prismatic specimens

▪ Compressive strength and thermal conductivity tests Uniaxial compressive strength (UCS) test was carried out using an IGM press, with a capacity of 250 kN (Fig. 8). The loading speed used is 0.4 kN/s. Mechanical performance of structural cob is determined for a strain of 2 %. This choice was made to have representative values of in-situ material. Three prismatic specimens (300 x 300 x 70 mm) of each lift were used to measure thermal conductivity of cob with a Heat Flow Meter from Netzsch (HFM 436 Lambda). The measurement was performed at 20 °C (Fig. 8).

Fig. 8: Compressive strength test (Left) and thermal conductivity test (Right)

▪

Cob mix and fibre content Currently, on-site cob mixing is made using volumetric material estimation. To monitor the mix variation during building, flax straw fibre content was determined. The experimental protocol consists in dissolving three cylindrical specimens for three lifts, in water and sieving the materials. Sieving size used are 6.3 -5 -3.15 -2.5 -0.9 and 0.5 mm, see Fig. 9.

Fibre content in cob mix

In the laboratory, the percentages of flax straw fibre measured for three lifts in relationship with the dry mass of soil are presented in Tab.2. The cob mixes on the site were done in volumetric estimation, which leads to variations in the amount of fibre in lifts.

ICBBM2021

Tab. 2: Fibre content measured in laboratory

Water content in cob mix and walls

According to the water contents measured in three cylindrical and three prismatic specimens of three lifts, there is not a significant variation in the water content, with the range of 18.4 % to 21.9 % (Fig. 10). The maximum water content in the walls is 21 %, as can be seen in Fig. 11.

Fig. 10: Average water content of the three lifts

Comparison between water content determined in cylindrical and prismatic specimens (Fig. 10) and those measured for walls (Fig. 11) confirm that there is not a large variation in water content. This moderate variation is due to the difference of initial water quantity added to the mix on site, building duration of lift (2 to 3 days) and weather condition (Temperature and relative humidity).

Fig. 11: water content of the different building walls

Dry density

After removal from the mold, they were oven dried at 40°C until they reached equilibrium weight; three consecutive weighing's at 24 hours intervals were within 1 % of each other (average 21 days). Densities of cylindrical and prismatic specimens are presented in Tab 3.

Tab. 3: Dry density of cylindrical and prismatic specimens The variation of dry density shown in Fig. 12 is ranging between 1660 and 1873 kg/m 3 in (3) lifts. This variation is probably due to the quantity of fibre in the mix and the material compaction. In lift 2, the presence of 3.7 % flax straw affects the dry cob and makes it less dense. The results of thermal conductivity measurement present values ranging between 0.610-0. 816W.m -1 •K -1 (Fig. 14). This variation of thermal conductivity results is in a relationship with a dry density of the prismatic specimens.

CONCLUSION

In this paper, monitoring and analysis of cob mixes have been done with control of four parameters: water content, dry density, compressive strength, and thermal conductivity. Results show that:

✓ Mix variations during construction lead to different densities and, consequently, to variable maximum compressive strengths range from 0.99 to 1.38 MPa and thermal conductivities from 0.610 to 0.816 W.m -1 •K - 1 .

✓ The variation in water content did not have a significant effect on the compressive strength, but it may affect the performance of the mixture throughout the contact between lifts. Particularly during drying, creating cracks. The monitoring of cob during three different lifts achieved by material characterizations can give a clear idea about the real mechanical and thermal performances of the prototype building; also, the preparation of homogenous and similar cob mix for each lift is important to ensure similar building properties. This work will continue all along the prototype building.

Fig. 2 :

 2 Fig. 2: Ground plan of the earth-based building

Fig. 3 :

 3 Fig. 3: Granulometric curve of the studied soilsFibre used in this study is natural flax straw, see Fig.4, they shows water absorption of 431 % after 24 hours, see Fig.5. This characteristic influences the mix properties at fresh state (absorption of available water) and at long term (hygrometric balance). Tensile strength tests on flax straw fibre were carried out under ambient conditions. Fibres of flax straw with 10 cm length were used to measure the tensile strength. The maximum tensile strength is 112 MPa[START_REF] Phung | Formulation and characterization of an earth-plant fibre composite[END_REF]. The average absolute density of flaw straw is 1300 kg/m 3 .

Fig. 9 :

 9 Fig. 9: flax straw fibre and soil separation protocol 3 RESULTS AND DISCUSSION

Fig. 12 :

 12 Fig.12: Average dry density of the three lifts3.4 Compressive strength and Thermal conductivityThe compressive strength results are presented in Tab.4 and Fig.13. They have not shown a large variation in maximum compressive strength [σ(max)] with range of 0.99 to 1.38 MPa and for a strain of 2 % [σ2%] with range of 0.96 to 1.20 MPa. Lift 2 presents a low compressive strength compared to the two others, probably due to the high amount of fibre in the mix and the compaction. According to the New Zealand earth construction standard NZS 4298[New Zealand Standards 1998], the shape of specimen, height to thickness ratio is 2.0, representing aspect ratio factor (Ka) of 0.80, specified by the standard NZS 4298. With: σ NZS 4298 = σ(max) * 0.7/0.8. The compressive strength results in three lifts are close to the minimum value "1.3 MPa" of New Zealand Standards 1998.Tab. 4: Dry density and compressive strength in 3 lifts

Fig. 14 :

 14 Fig. 14: Cob thermal conductivity in 3 different lifts

ACKNOWLEDGMENTS

This work is part of the CobBauge project selected by the European cross-border cooperation program INTEREG V France-(Channel)-England. The authors would like to thank the ERDF for the financing of this work.


J.-M Friedt
email: jmfriedt@femto-st.fr

W Feng

D Rabus

G Goavec-Merou

Real time GNSS spoofing detection and cancellation on embedded systems using Software Defined Radio

Keywords: software defined radio, spoofing cancellation, jamming cancellation

Global Navigation Satellite Systems have become ubiquitous to most daily activities requiring Positioning, Navigation and Timing and yet have become increasingly subject to spoofing and jamming, partly due to the availability of affordable software defined radio platforms allowing such functionalities. Despite novel modulation schemes and broader frequency bands, GPS L1 at 1575.42 MHz remains the main signal source for most consumer grade receivers. We address here real time spoofing and jamming mitigation using a software defined radio approach in which the raw (I, Q) coefficients collected by the radiofrequency frontend are analyzed for spoofing detection by assessing the phase difference of the GPS signals collected by different antennas. These signals are possibly cleaned from the interfering sources by null-steering when spoofing or jamming is detected, and the cleaned signal are used for real time Position and Navigation information extraction using the opensource gnss-sdr framework. The application to other frequency bands is considered for redundancy and detecting spoofing attacks. Timing information is under consideration as well.

I. INTRODUCTION: SOFTWARE DEFINED RADIO BASED CRPA

Software Defined Radio (SDR) provides the means for widespread spoofing capability with minimum financial investment, while the ubiquity of Global Navigation Satellite Systems (GNSS) leads to increasing impact of such threat [START_REF]The economic impact on the UK of a disruption to GNSS[END_REF]. Despite the advent of new constellations with advanced security on the digital messages, legacy receivers are expected to be in use for the coming years. Thus, mitigation of spoofing and jamming threats on the GPS L1 band is addressed using SDR receivers [START_REF] Kumara | Improved real time GPS RF data capturing for GNSS SDR applications[END_REF] and more specifically the opensource gnss-sdr SDR GNSS decoding software running on top of the opensource GNU Radio framework.

CRPA (Controlled Radiation Pattern Receiver) is a well known null-steering approach to jammer and spoofer cancellation in GNSS prone to such attacks considering the weak signals reaching the ground from satellites over 20000 km away from the receiver, actually below thermal noise. Signal processing techniques require on the one hand to identify the characteristics of the spoofer or jammer, and on the other hand to tune the phase between various antenna elements to cancel this contribution and allow for recovering the genuine signal. While classically performed in the analog domain (e.g. radiofrequency phase shifters), software defined radio GNSS receivers are well suited to such a task since providing access to the raw (I, Q) samples and hence the physical characteristics of the incoming electromagnetic waves. In this paper, we demonstrate real time jamming and spoofing cancellation using a codeless demodulation technique implemented at the signal acquisition level of gnss-sdr to process the datastream and provide Navigation and Positioning information from the genuine constellation despite interfering sources. All experimental demonstrations are performed with two antennas in clear sky view of the genuine GPS constellation with spoofing and jamming signals generated nearby and transmitted wirelessly in a scenario representative of practical attacks (Fig. 1), weak enough for this illegal emission demonstration to hardly disturb legal receivers beyond the university building. The extension to other frequency bands is addressed and the current limitation of SDR is discussed in the context of Commercial, Off The Shelf (COTS) hardware.

II. SPOOFING DETECTION AND CANCELLATION

The benefit of a SDR approach to GNSS signal processing is the access to the raw radiofrequency datastream allowing to characterize the electromagnetic wave propagation properties.

The (I,Q) datastream generated after radiofrequency frequency transposition to baseband and sampling by the analog to digital converters contains the sum of the contributions of all satellite signals. Spoofing attack detection, assuming a single spoofing source, will rely on analyzing direction of arrival (DoA) of the various contributions after sampling with multiple antennas the signal (Fig. 2). In our demonstration, an Ettus Research B210 dual-channel receiver is used, sampling at 1.123 MS/s the L1 band centered on 1575.42 MHz with 12-bit resolution. The AD9361 radiofrequency frontend fitted on this SDR platform clocks both receiver input channels with the same local oscillator, allowing for phase analysis to extract DoA information. θ0 θ1

d d φ=2π /λ θ sin Fig. 2.
Basic principle of the CRPA approach: a genuine constellation will exhibit a diversity of directions of arrival measured as a diversity of phases between two antennas collecting GNSS signals. A unique spoofer might mimic the Doppler shift, code and navigation messages of the constellation but the phase difference between antennas, defined by electromagnetic wave propagation physics, cannot be spoofed. d is the distance between antennas inducing a phase difference ϕ between the two antennas illuminated by the signals at angle θ which is practically a combination of azimuth and elevation.

Since GNSS Code Division Multiple Access (CDMA) encoded Binary Phase Shift Keying (BPSK) modulation spreads power below thermal noise, DoA measurement requires removing the CDMA modulation to bring the relevant information above thermal noise. The two general approaches to spoofing detection by analyzing the radiofrequency wave characteristics involve pre-and post-despreading computation, where either the full GNSS correlation can be performed in the latter case or where compression is computed prior to correlation with no knowledge of the CDMA characteristics. The former approach, also known as codeless analysis, is considered here for its computational efficiency. Indeed, BPSK modulation is canceled by squaring the signal, so that the modulated phase equal to 0 or π doubles as the argument of the squared complex (I + jQ) become 0 modulo 2π and the energy is concentrated in the carrier raising above the thermal noise. While the Pseudo Random Number (PRN) sequence encoding which Space Vehicle signal was received has been lost in the process, the Doppler shift still allows for differentiating the various satellite contribution to the signal. Even a properly spoofed signal will introduce realistic Doppler shifts for each satellite signal, consistent with its azimuth and elevation. However, the phase difference between antennas is only determined by plane wave propagation characteristics and cannot be spoofed. Thus, observing the phase at the various frequency offsets of the discrete peaks in the Fourier transform at double the Doppler shift (due to the squaring process) allows for assessing spoofing. If all phases are close, despite satellites being distributed in azimuth and elevation as would be expected from a genuine constellation, then spoofing is identified. Sorting the phases and analyzing their standard deviation is used for automating the spoofing detection.

Formally, considering that antenna a receives satellite i signal as

s a (t) = A a (t) exp(j( δω i t Doppler + ϕ P RN,i BP SK + ϕ a,i geometric )) (1)
with Doppler shift δω i , BPSK modulated code ϕ P RN,i ∈ [0, π], and geometrical phase ϕ a,i , then squaring the signal cancels the PRN modulation and the associated energy spreading as

s 2 a (t) = A 2 a (t) exp(j(2δω i t + 2ϕ a,i )) (2)
and the contributions of the various satellites are separated by their different Doppler shifts in the Fourier transform of the squared signals. The weight of the spoofing signal is deduced from the ratio of these (complex) coefficients

F F T (s 2 1 (t)) F F T (s 2 2 (t)) = A 2 1 (t) A 2 2 (t) exp(2j(ϕ 1,i -ϕ 2,i )) (3)
computed at bin δω i of the Fourier transform. If all ϕ 1,i -ϕ 2,i ∀i are close, a spoofing attack is detected through a threshold on the standard deviation analysis on ϕ 1ϕ 2 (Fig. 3). As adjacent antenna phase and relative magnitude is computed as argument and module of each Fourier transform of the squared signal spectral component, identifying spoofed contributions as the signals with close phases allows for identifying the DoA and hence for null-steering by using destructive interference in this direction to cancel the spoofing signal. Applying this processing after analog-to-digital conversion requires a high resolution sampling as the genuine signal must remain above the least significant bit quantization level while avoiding saturation of the received signal including the possibly strong spoofing contribution. Thus, classical low-resolution (1-or 2-bit resolution as provided for example by Maxim IC MAX2771 receiver) sampling techniques classically used for GNSS reception might not be suitable for spoofing (and jamming) cancellation as post-processing steps.

III. COMPUTATIONALLY EFFICIENT ALGORITHMS IMPLEMENTATION IN gnss-sdr

These concepts are implemented in the early data collection of gnss-sdr for real time processing and decoding. While gnss-sdr is organized in a strict hierarchy of processing planes from Signal Source to Position, Velocity and Time extraction with intermediate processing steps including Signal Conditioning, Acquisition, Tracking, and Decoding, we have included the jamming and spoofing cancellation in the Signal Source acquisition in order to accept data streaming from multiple sources. Indeed, CRPA requires multiple antennas to identify and cancel the interfering signal, in our case the datastream from the two channels of the B210 receiver. Running efficiently on an embedded board requires crosscompiling gnss-sdr to the embedded General Purpose Central Processing Unit (GP-CPU) with maximum performance: gnss-sdr is included in the Buildroot framework allowing the use of the SIMD instruction set of the processor fitted on the Raspberry Pi4 single board computer. These SIMD instruction are heavily used by the Vector Optimized Library of Kernels (VOLK) developed as part of the GNU Radio framework with 3 to 7-fold speed improvement over the generic function implementation [START_REF] Friedt | Software defined radio based global navigation satellite system real time spoofing detection and cancellation[END_REF]. As the handled quantities are best represented as vectors, using the VOLK implementations such as volk_32fc_x2_multiply_32fc() for squaring complex vectors or volk_32fc_x2_divide_32fc() for computing the ratio of the Fourier transforms of the two squared channels samples provides significant computational speed benefit. Jamming cancellation cannot benefit from the known structure of the interfering signal and a least square (LS) approach is used to identify the contribution of the jamming signal on one antenna and subtract it from the second antenna: rather than implement a matrix pseudo-inverse, a stochastic gradient descent algorithm has been implemented to iteratively converge towards the complex weight identification. Alternatively, the 0-delay coefficient of the cross-correlation function computed as the ratio of the Fourier transforms (rather than the product of the complex conjugate since here the ratio of the magnitudes is required to identify the weight) followed by an integration as an efficient computation of the 0-delay inverse Fourier transform coefficient is implemented as jamming cancellation algorithm (Tab. I): this inverse filtering approach has been used for demonstration on a moving jammer (Fig. 4).

IV. MULTIBAND APPLICATION

One protection against spoofing or jamming is redundancy by analyzing the information transmitted on multiple frequency bands. While the civilian L1 band around 1575.42 MHz is most commonly used, the multiple constellations (GLONASS, Beidou, Galileo) will allow to address spoofing attacks by assessing consistency of the signals, and each one of these constellations is transmitting on multiple frequency bands. Nevertheless, a careful enough attacker might generate spoofing signals in all such frequency bands, and extending the CRPA approach to all bands is desirable. One limitation of SDR is the signal bandwidth limited by digital communication bandwidth: with GNSS bands separated by more than 400 MHz, multiple receivers are needed to address all bands.

We consider the L5 band around 1176.45 MHz as a complement to L1 reception (Tab. II), despite fewer satellite currently emitting this signal (Fig. 5). L5 uses a 10-times longer PRN code than L1 C/A so that the bandwidth is increased 10-fold. Furthermore, the Binary Offset Carrier (BOC) modulation leads to a poorer signal to noise ratio when applying a basic codeless processing technique [START_REF] Borio | Codeless processing of binary offset carrier modulated signals[END_REF], [START_REF] O'driscoll | Codeless code tracking of BOC signals[END_REF], and hence requires longer integration time to raise the signal above noise in a codeless decoding approach (Fig. 6). Nevertheless, the underlying BPSK modulation remains cancelled by squaring and the same processing technique can be used as was demonstrated with L1 (Fig. 7). Similarly, L2 around 1227.6 MHz now transmits a civilian signal which is considered as a complement to L1 and L5, with a narrower bandwidth better suited for SDR investigations. All these signals are collected by feeding one input channel of the B210 receiver with the output of a Septentrio PolaNt-x MF broadband antenna. Samples were collected either with a Dell Precision M6500 laptop or a Raspberry Pi4, in both cases using the USB3 port. As was done with L1, we consider a 10% margin when selecting the sampling rate and 11.23 MS/s is used to record L5 I/Q data for post-processing. Indeed, we have observed that real time processing is beyond the current GP-CPU fitted in consumer electronics computers and post-processing is used in this demonstration. The B210 frontend, Analog Devices AD9361, samples I and Q coefficients with 12-bit resolution, leading to 16-bit complex data requiring 32-bit transfers for each sample. At a sampling rate of 11.23 MS/s, the resulting data-rate of 44.92 MS/s is observed to be beyond the bandwidth of the USB3 bus, and reducing the data resolution to 8-bit is mandatory to avoid sample loss.

The L5 signal quality was assessed by running a postprocessing analysis using gnss-sdr. Indeed, the position was retrieved from the collected data

Fig. 1 .

 1 Fig. 1. Experimental setup: an Ettus Research B210 Software Defined Radio platform is fed through bias-T polarization circuits by the signal collected by active antennas in clear view of the sky. The resulting datastream is transmitted through the USB3 bus to a Raspberry Pi4 (RPi4) single board computer. The antennas are attached to a rotating, FR4 copper-coated printed circuit board, ground plane to simulate the motion of the spoofing or jamming source with respect to the CRPA array.

Fig. 3 .

 3 Fig. 3. Left: spoofing of the GPS constellation in the L1 frequency band with the antennas facing the sky: most phases are the same with only a couple of genuine satellites detected, indicative of a spoofing attack. Right: genuine constellation exhibiting the diversity of phases representative of the diversity of the directions of arrival.

Fig. 4 .

 4 Fig.[START_REF] Borio | Codeless processing of binary offset carrier modulated signals[END_REF]. Moving jammer cancellation: the set of antennas is positioned on a rotating stage in clear view of a sky. A nearby jammer prevents the signal acquisition if no cancellation occurs. After initial position acquisition, the jammer is switched on at date 110 s, the antenna rotated by 90 • at date 180 s and back to its initial orientation at date 220 s, and finally the jamming source is switched off at 290 s.

Fig. 5 .

 5 Fig. 5. GPS constellation configuration during the L5 acquisition experiment: the useful satellites have been highlighted in red in the edited screenshot of GPSTest software running on a Sony Z5 mobile phone.

  Fig. 6. Codeless analysis of the L1 (top), L2 (middle) and L5 (bottom) signals sampled at 1.123 MS/s (L1 and L2) or 11.23 MS/s (L1, L2 and L5 twice) sequentially. The various satellites are visible as different Doppler shifts (frequency offsets) for L1 and L2 BPSK modulation but the signal to noise ratio is insufficient for the L5 modulation in these 1-second integration durations. Notice that the abscissa is twice the Doppler shift due to squaring the signal.

[

  ...] Current receiver time: 38 s New GPS L5 CNAV message received in channel 0: ephemeris from satellite GPS PRN 01 (Block IIF) New GPS L5 CNAV message received in channel 1: ephemeris from satellite GPS PRN 03 (Block IIF) New GPS L5 CNAV message received in channel 2: ephemeris from satellite GPS PRN 04 (Block Unknown) New GPS L5 CNAV message received in channel 4: ephemeris from satellite GPS PRN 09 (Block IIF) First position fix at 2020-Oct-10 11:18:19.240000 UTC is Lat = 47.2513 [deg], Long = 5.99305 [deg], Height= 431.628 [m]

Fig. 7 .

 7 Fig. 7. Codeless analysis of the L1 (top), L2 (middle) and L5 (bottom) signals sampled at 1.123 MS/s (L1 and L2) or 11.23 MS/s (L1, L2 and L5) sequentially. The various satellites are visible as different Doppler shifts (frequency offsets) for all bands thanks to the longer, 30 seconds, integration duration. The 11.23 MS/s L5 acquisition was repeated twice. Notice that the abscissa is twice the Doppler shift due to squaring the signal.

TABLE I COMPARISON

 I OF THE WEIGHT COMPUTATION USING INVERSE FILTERING (IF AS INTEGRAL OF THE RATIO OF THE FOURIER TRANSFORMS AS 0-DELAY CORRELATION) AND THE STOCHASTIC GRADIENT LEAST SQUARE (LS) METHOD. NOTICE THE CONSISTENCY OF THE COMPLEX WEIGHT WITH A LARGE MODULE IN CASE OF JAMMING (LEFT) AND THE NEGLIGIBLE WEIGHT IN THE ABSENCE OF JAMMING (RIGHT): A

	THRESHOLD IS USED FOR JAMMING DETECTION AND APPLYING THE
	CANCELLATION BY SUBTRACTING THE WEIGHTED MEASUREMENT FROM
	ONE ANTENNA TO THE SECOND ANTENNA SIGNAL AS JAMMING LEADS TO
	1000-FOLD STRONGER WEIGHTS.
	With jamming	No jamming
	IF 0.1809+i * 0.3856	IF 0.0606+i * -0.0125
	LS (0.2191,0.4710)	LS (0.00027,0.00011)
	LS (0.2196,0.4684)	LS (0.00037,0.00014)
	IF 0.1779+i * 0.3678	LS (0.00046,0.00018)
	LS (0.2125,0.4629)	IF 0.06623+i * 0.00309
	LS (0.2127,0.4668)	LS (0.00064,0.00026)
	IF 0.1781+i * 0.3618	LS (0.00073,0.00029)
	LS (0.2121,0.4662)	LS (0.00082,0.00033)
	LS (0.2129,0.4649)	LS (0.00092,0.00036)
	LS (0.2138,0.4657)	IF 0.06576+i * 0.01101

TABLE II L1

 II SIGNAL SAMPLED WITH 8-BIT RESOLUTION AT 11.23 MS/S AND DECODED USING gnss-sdr WITH THE CONTRIBUTION OF SPACE VEHICLES CONSISTENT WITH THE LIST SHOWN IN FIG. 5.

	New GPS NAV message received in channel 2: subframe
	3 from satellite GPS PRN 04 (Block Unknown)
	New GPS NAV message received in channel 4: subframe
	3 from satellite GPS PRN 09 (Block IIF)
	New GPS NAV message received in channel 8: subframe
	3 from satellite GPS PRN 19 (Block IIR)
	New GPS NAV message received in channel 6: subframe
	3 from satellite GPS PRN 17 (Block IIR-M)
	New GPS NAV message received in channel 1: subframe
	3 from satellite GPS PRN 03 (Block IIF)
	Current receiver time: 1 min 19 s
	First position fix at 2020-Oct-10 11:29:30.300000 UTC
	is Lat = 47.2516 [deg], Long = 5.99321 [deg], Height=
	338.448 [m]
	Position at 2020-Oct-10 11:29:30.500000 UTC using 4
	observations is Lat = 47.251578218 [deg], Long =
	5.993222004 [deg], Height = 336.211
	Velocity: East: -0.890 [m/s], North: -1.676 [m/s],
	Up = 3.507
	Position at 2020-Oct-10 11:29:31.000000 UTC using 4
	observations is Lat = 47.251630849 [deg], Long =
	5.993326694 [deg], Height = 299.172

ACKNOWLEDGMENT

This work was supported by the French National Research Agency (ANR) grant FAST-LAB dedicated to developing technologies for secure time transfer. Support from the Oscillator Instability Measurement Platform (OscIMP) is acknowledged.

although real time acquisition and processing of the L5 band is beyond its processing capability. In powersave configuration (600 MHz CPU speed), running gnss-sdr configured to analyze 5 channels required 2.9 times the acquisition duration to process the file stored in a RAM-filesystem (i.e. not limited by data transfer rates) and 2.4 times the acquisition duration when analyzing 4 channels, while in performance configura-tion (1500 MHz clock speed) the analysis duration dropped to 1.3 times the acquisition duration. This ratio above 1 in addition to the burden of USB data transfer prevents real time L5 acquisition and processing even with the highest processing speed on the Raspberry Pi4, excluding spoofing and jamming mitigation processing: with the "O" generated by GNU Radio indicating Overflow of the signal source due to too slow processing of the generated samples.

V. CONCLUSION

We have implemented CRPA algorithms for detecting and cancelling in real time L1 GPS spoofing and jamming in the gnss-sdr software defined radio framework. Computationally efficient methods allow for this software to run on the Raspberry Pi4 embedded platform. Other frequency bands, namely L5, with larger processing requirements, are addressed to complement spoofing mitigation by assessing redundancy of the decoded information: these results are achieved at the post-processing level as current computational power is insufficient for real time analysis in parallel to data collection. The forked repository of gnss-sdr implementing the algorithms described in this paper is available at https: //github.com/oscimp/gnss-sdr.