Naima Zerari
email: n.zerari@univ-batna2.dz

Samir Abdelhamid
email: s.abdelhamid@univ-batna2.dz

Hassen Bouzgou
email: h.bouzgou@univ-batna2.dz

Christian Raymond
email: christian.raymond@irisa.com

Bi-directional Recurrent End-to-End Neural Network Classifier for Spoken Arab Digit Recognition

Keywords: digits, Speech recognition, Auto-encoder, Mel Frequency Cepstral Coefficients, Long Short-Term Memory, Multilayer perceptron network

Automatic Speech Recognition can be considered as a transcription of spoken utterances into text which can be used to monitor/command a specific system.

In this paper, we propose a general end-to-end approach to sequence learning that uses Long Short-Term Memory (LSTM) to deal with the non-uniform sequence length of the speech utterances. The neural architecture can recognize the Arabic spoken digit spelling of an isolated Arabic word using a classification methodology, with the aim to enable natural humanmachine interaction. The proposed system consists to, first, extract the relevant features from the input speech signal using Mel Frequency Cepstral Coefficients (MFCC) and then these features are processed by a deep neural network able to deal with the non uniformity of the sequences length. A recurrent LSTM or GRU architecture is used to encode sequences of MFCC features as a fixed size vector that will feed a multilayer perceptron network to perform the classification. The whole neural network classifier is trained in an end-to-end manner. The proposed system outperforms by a large gap the previous published results on the same database.

I. INTRODUCTION

Automatic speech recognition (ASR) is the process of understanding the human speech by a computer. In this context, Automatic Digit Recognition (ADR) is considered as one of the most challenging domains in ASR. The increasing importance of digit recognition is mainly due to the increasing demand for applications that deal with human-machine interaction through natural languages such as command systems via pronounced digit [START_REF] Davis | Automatic recognition of spoken digits[END_REF].

The implementation of these kinds of systems necessitate a specific process for the speech signal to deliver reliable features that can identify correctly the input speech utterances . Accordingly, a wide range of techniques have been proposed in the literature to parametrically represent the speech signal [START_REF] Desai | Feature extraction and classification techniques for speech recognition: A review[END_REF].

The most commonly used one, is the Mel-Frequency Cepstral Coefficients (MFCC), which is a popular technique that try to mimic some parts of the human speech production and speech perception [START_REF] Juang | Fundamentals of speech recognition[END_REF].

In the present work, the resulted MFCC coefficients of the spoken Arabic digits will be fed to a Long Short-Term Memory (LSTM) architecture which deals with general sequence to sequence problems. The idea is to use the bidirectional LSTM layer to encode the sequence as a fixed size vector, then this vector will be introduced to a multilayer perceptron (MLP) classifier to perform the recognition task. The whole proposed model is trained in an end-to-end way with the aim to improve the recognition rate of the classification model on the arab digit dataset [START_REF] Lichman | UCI machine learning repository[END_REF].

The remainder of the paper is organized in six sections as follows: Section II highlights some related works. Section III explains the methodology proposed in this study. Section IV presents the data used to validate the proposed methodology. Section V presents the performance criteria used to evaluate the proposed model. Section VI explains the experimental setup of the proposed classifier and presents the experimental results obtained on the data set, and compares with other existing approaches in the literature. Finally, section VII draws the conclusion of this work.

II. RELATED WORKS

Several studies have been investigated in the literature to improve the recognition accuracy of an ASR system using different approaches [START_REF] Gupta | The state of the art of feature extraction techniques in speech recognition[END_REF]. Therefore, the number of research in Arabic language is limited compared to other languages such as English. In what follows, some studies concerning ASR systems for Arabic language will be discussed. The performance of each model varies in terms of accuracy. In [START_REF] Saeed | A speech-and-speaker identification system: feature extraction, description, and classification of speechsignal image[END_REF], the authors proposed a speech-and-speaker (SAS) identification system based on spoken Arabic digit recognition. They treated the speech signals as an image object and used conventional and Artificial Neural Networks (ANN) methods for classification and the algorithm of Teplitz matrix minimal eigenvalues as feature extraction method. In [START_REF] Hammami | Tree distribution classifier for automatic spoken arabic digit recognition[END_REF], the authors described an automatic discrete speech recognition system based on a tree distribution classifier. The MFCC feature extraction method was used to extract features followed by a vector quantization method (VQ). The VQ output was provided as an input to a classifier, which deliver the class-label according to each feature using an optimal spanning tree model in order to approximate the true class probability.

In [START_REF] Hammami | Improved tree model for arabic speech recognition[END_REF], the authors introduced a fast learning method with a graphical probabilistic model for discrete speech recognition based on spoken Arabic digit recognition. The proposed method based on spanning tree structure takes advantage of the temporal nature of speech signal. The obtained results suggests that the proposed method was efficient in terms of time computation than the state-of-the-art algorithms that use the maximum weight spanning tree (MWST). In [START_REF] Daqrouq | Wavelet lpc with neural network for spoken arabic digits recognition system[END_REF], the authors proposed an Arabic digits classifier system with 450 Arabic spoken digits. The system is based on combining wavelet transform with the linear prediction coding method (LPC) to extract the features and the probabilistic neural network (PNN) for classification. The proposed classifier provided a high recognition rate, reaching about 93% of accuracy based on a speaker-independent system. Recently, excellent performances on these systems have been achieved using Deep Neural Networks (DNNs) which are recent and extremely powerful machine learning models [START_REF] Graves | Speech recognition with deep recurrent neural networks[END_REF].

III. METHODOLOGY

Arabic is the native language of twenty-five countries including Algeria. It represents a Semitic language, and it is one of the oldest languages in the world. Currently it is the fifth language in terms of number of speakers [START_REF] Abuzeina | Arabic speech recognition systems[END_REF]. The proposed system is based on the following steps, depicted in Fig. 1 below.

A. MFCC feature extraction

To recognize the pronounced word; the speech waveform is converted into a parametric representation by using a specific technique. Thus, the reliability of Mel-Frequency Cepstral Coefficient (MFCC) technique to extract features from speech has been investigated. This technique introduced by Davis and Mermelstein in the 1980's, and have been widely used [START_REF] Mamta | Noise robust speech recognition system using mel cepstral and genetic algorithm[END_REF].

It is based on the frequency domain of Mel scale to capture the important characteristics included in the speech. Generally, MFCC is used to reduce the dimensionality of the acoustic signal while maintaining its discriminating power [START_REF] Reynolds | An overview of automatic speaker recognition technology[END_REF].

Thus, the feature extraction goal is to give a useful representation of the speech signal by capturing the significant information from it. The steps involved in MFCC feature The main steps to compute MFCC coefficients are presented below:

• Pre-emphasized: The speech is, first, pre-emphasized with a pre-emphasis filter 1-az -1 to spectrally flatten the signal. In the time domain, the relationship between the output and the input of the pre-emphasis block is given in equation [START_REF] Davis | Automatic recognition of spoken digits[END_REF].

Y (n) = X(n) -aX(n -1), (1)
where a is the factor which takes a value in the range [0.9, 1], however, the default value is 0.97 [START_REF] Han | An efficient mfcc extraction method in speech recognition[END_REF], X(n) is the input signal and Y (n) is the output signal.

• Framing: the pre-emphasized speech is separated into short segments called frames. The frame length is sets to 20 ms to guarantee stationarity inside the frame (Speech must remain stationary throughout the period of analysis).

An overlap of 50% ( 10ms) is done between two adjacent frames to ensure the stationary between frames [START_REF] Han | An efficient mfcc extraction method in speech recognition[END_REF], [START_REF] Ali | Database development and automatic speech recognition of isolated pashto spoken digits using mfcc and k-nn[END_REF]. • Windowing: is an important step to minimize the signal discontinuities at the beginning and the end of the each frame. For this propose, Hamming window is used to reduce the edge effect. It is expressed via the following mathematical expression [START_REF] Ali | Database development and automatic speech recognition of isolated pashto spoken digits using mfcc and k-nn[END_REF].

Y (n) = X(n) * W (n), (2)
W (n) = 0.54 -0.46 cos 2πn N -1 , ( 3
)
where N represents the number of samples in each frame (window length) and n is from 1 to N .

• FFT: Fast Fourier Transform is applied to each frame to convert the N samples from time domain to frequency domain.

• Mel filter bank is applied to calculate the average energy in each block and take the logarithm of all filter bank energies by using one of the mathematical expressions below.

M el(f ) = 2595 * log 10 (1 + f /700), (4)
or

M el(f ) = 1127 * ln(1 + f /700), (5)
Applying triangular filters on Mel-scale to the power spectrum to extract frequency bands. The Mel-scale aims to mimic the non-linear human ear perception of sound. These coefficients are highly correlated. • The log Mel spectrum will be converted back to time domain. The result is called the Mel frequency cepstrum coefficients (MFCC). A good representation of the local spectral properties of the signal for the given frame analysis is delivered by the cepstral representation of the speech spectrum. Using the discrete cosine transform (DCT), the Mel spectrum coefficients are converted to the time domain. [START_REF] Anusuya | Front end analysis of speech recognition: a review[END_REF].

B. Recurrent neural networks and gated recurrent neural networks

Recurrent Neural Networks (RNN) are a type of neural networks with recurrent connections. RNNs have been widely studied and used for problems involving input and output data of variable size and are particularly suitable for time series analysis and consequently used in automatic speech recognition [START_REF] Chung | Gated feedback recurrent neural networks[END_REF], [START_REF] Vukotic | A step beyond local observations with a dialog aware bidirectional GRU network for Spoken Language Understanding[END_REF]. The success of this type of neural network is due to the specific variant, which are the long shortterm memory (LSTM) proposed by Sepp Hochreiter and Jrgen Schmidhuber in 1997 [START_REF] Chung | Empirical evaluation of gated recurrent neural networks on sequence modeling[END_REF] and the gated recurrent unit (GRU) proposed by Kyunghyun Cho et al in 2014 [START_REF] Chung | Gated feedback recurrent neural networks[END_REF]. The main idea behind these networks is to use several gates to control the information flow from previous steps to the current steps [START_REF] Gao | Deep gate recurrent neural network[END_REF].

By employing the gates, any recurrent unit can learn a mapping from one point to another. LSTM, contains three gates: an input gate, an output gate and a forget gate. At each iteration, the three gates try to remember when and how much the information in the memory cell should be updated [START_REF] Gao | Deep gate recurrent neural network[END_REF]. A single LSTM memory cell is depicted in figure 3 [START_REF] Huang | Bidirectional lstm-crf models for sequence tagging[END_REF] .

C. Multi-layer perceptron classifier

Multilayer perceptrons (MLP) is the most commonly used feedforward neural networks. The MLP architecture is variable, but generally organized in several layers of neurons. It consists of three sequential layers: input, hidden and output layer as depicted in figure 4. The input layer serves to feed the input vector to the network. The hidden layer output a non-linear transformation of the input in order to facilitate the determination of the last transformation computed by the output layer [START_REF] Pal | Multilayer perceptron, fuzzy sets, and classification[END_REF].

The MLP neural networks classifier acts usually in a supervised manner, to build an MLP classifier, a set of training data including the inputs and their associated outputs are requested. Hence, the classification is done by assigning a maximum value to the output neurons to represent the desired class [START_REF] Bishop | Neural networks for pattern recognition[END_REF].

Where

• f t = σ g (W f x t + U f h t + b f ) • i t = σ g (W i x t + U i h t-1+bi ) • o t = σ g (W o x t + U o h t-1 + b o ) • c t = f t • c t-1 + i t oσ c (W c x t + U c h t-1 + b c ) • h t = o t • σ h (c t ) • • is

IV. EXPERIMENTAL DATA

The experiments are done using the Spoken Arabic Digit dataset grouping the Arabic digit from 0 to 9 as illustrated in figure 5 [4]. This dataset contains time series of MFCCs corresponding to spoken Arabic digits collected by the laboratory of automatic and signals, University of Badji-Mokhtar -Annaba, Algeria. A number of 88 individual (44 males and 44 females) Arabic native speakers were asked to utter all digits ten times. Accordingly, the database consists of 8800 tokens (10 digits x 10 repetitions x 88 speakers) [START_REF] Hammami | Improved tree model for arabic speech recognition[END_REF]. The whole dataset is divided into two parts: a training set Fig. 5: Arabic Digit and their writing.

with 75% of the samples (6600 tokens) and the test set with the remaining 25% samples (2200 tokens).

The Mel Frequency Cepstral Coefficients were computed with the following parameters illustrate in Table I. Different performance measures can be used to evaluate the performance of the ASR systems [START_REF] Jiang | Confidence measures for speech recognition: A survey[END_REF]. In our study, we have adopted the following standard classification criteria: recall, precision, f-measure (F1), % of error and % of success. They are defined below: precision = number of correct predictions number of predictions recall = number of correct predictions number of samples

F 1 = 2 * precision * recall precision + recall

VI. EXPERIMENTAL SETUP OF THE END-TO-END ENCODER-CLASSIFIER

The proposed end-to-end neural network that takes as input the sequences of MFCC features and as output the class of the spoken digit. First, the network will encode the sequence of MFCC coefficients as a fixed size vector. This fixed sized vector will feed an MLP network to classify the MFCC coefficients. To do so, we, first, encode the data as a matrix of (6600,93,13) where 6600 is the size of samples in our training data, 93 is the size of the longest sequence of MFCC coefficients (corresponding to the long time of recording) and 13 is the number of MFCC coefficients used in this study. When the size of the sequence is smaller than 93, the sequence is padded by a zeroed vector of size 13 until Next, the data are processed by a bidirectional LSTM layer to encode the sequence as a fixed size vector, we choose a bidirectional model since it has been shown that bidirectional models are in general more efficient than single direction models as it is proved by the results reported in table III [START_REF] Vukotic | A step beyond local observations with a dialog aware bidirectional GRU network for Spoken Language Understanding[END_REF]. The vector is then directed to a multilayer perceptron structure with one hidden layer. The diverse parameters have been fixed intuitively as follow:

• the output of the LSTM layer has been fixed to 50, the input sequence has been processed by an LSTM layer in a forward / backward direction by the other LSTM layer. The encoded fixed size vector is the concatenation of the 2 LSTM output vectors. • hidden layer size is set to 50 and non-linear activation function is set to Rectified Linear Unit (ReLU). • Output layer is set to 10, corresponding to the 10 classes of digits, with a standard softmax activation function using cross entropy loss. In order to regularize the network, two Dropout layers are inserted respectively after the LSTM output and the MLP hidden layer with a dropout probability of 0.2 and 0.5 respectively [START_REF] Srivastava | Dropout: A simple way to prevent neural networks from overfitting[END_REF].

This neural network is trained for 50 epochs with a batch size of 16, the final model chosen is the one that provides the best f-mesure performance on the training set. The figure 6 summarizes the topology of this neural network and the table IIa gives the performance averaged over 10 experiments of this model on the test set in terms of precision, recall and f-mesure for each class. The performance ( 98.77% of global f-measure) is actually close to perfect on this dataset and outperforms largely the previous published results reported on the same dataset [START_REF] Hammami | Tree distribution classifier for automatic spoken arabic digit recognition[END_REF], [START_REF] Hammami | Improved tree model for arabic speech recognition[END_REF] (see table IIb).

To confirm the choice of the bidirectional recurrent architecture as encoder, we evaluate different encoders using both combination of GRU and LSTM layers with forward, backward and bidirectional encoding strategies. We fix the size of the encoding vector at 100. We evaluate the performance of the network using forward, backward and bidirectional LSTM and GRU encoders in terms of f-measure and % error, The proposed strategies are listed below and the results are reported in the table III:

• Bidirectional LSTM of size 50; • Bidirectional GRU of size 50; • Forward GRU of size 100;

• Backward GRU of size 100;

• Forward LSTM of size 100;

• Backward LSTM of size 100; All encoder variants exhibit good results and outperform those by [START_REF] Hammami | Tree distribution classifier for automatic spoken arabic digit recognition[END_REF] and [START_REF] Hammami | Improved tree model for arabic speech recognition[END_REF] by at least 5% of accuracy. These experiments tend to confirm that bidirectional architectures are more efficient than single direction ones while having less parameters. It should be noted that, the processing sequences in backward direction looks also more efficient than those by forward direction.

VII. CONCLUSION

In this paper, an end-to-end approach based on recurrent neural networks to process sequences of variable lengths of MFCC features was presented. The extracted MFCC features sequences are, first, encoded as a fixed size vector by a recurrent LSTM/GRU neural network, next, the obtained vector was introduced to a standard multilayer perceptron to classify the spoken word. Based on the results and discussions presented in this paper, obtained on spoken Arabic digits, the obtained results confirm the effectiveness of the proposed model. In all the experiments carried out, the proposed system presents an improved performance with respect to some existing models in the literature and obtains promising results. The obtained results show that MFCC features (introduced to a classification system) are efficient enough to characterize the speech signal for this kind of tasks. The Challenge for future works, is to assess this kind of systems with noisy ( more realistic) speech signals.

Fig. 1 :

 1 Fig. 1: Block diagram of the proposed ASR system.

Fig. 2 :

 2 Fig. 2: MFCC Block diagram.

Fig. 3 :

 3 Fig. 3: A Long Short-Term Memory Cell.

Fig. 4 :

 4 Fig. 4: Multilayer perceptron architecture.

Fig. 6 :

 6 Fig. 6: Block scheme of the proposed end-to-end model.

TABLE I :

 I MFCC Parameters used in the experiments.

Parameters

Values Sampling rate 11025 Hz, 16 bits Filter pre-emphasized 1-0.97*Z -1 Applied window Hamming V. PERFORMANCE CRITERIA

TABLE II :

 II Results and comparison of the end-to-end neural approach with some previous published approaches on the same dataset.

						(b) Comparison with the approaches by [7]
	(a) Results with the end-to-end approach.	and [8] in terms of % success.
	dig. precision	recall	F1	%error	dig.	[7]	[8]	our BiLSTM
	0	95.98	98.73	97.33	4.14	0	91.00	85.55	95.86
	1	98.92	99.86	99.39	1.09	1	99.00	98.36	98.91
	2	99.63	98.91	99.27	1.09	2	91.50	92.91	98.91
	3	98.67	97.45	98.06	2.55	3	88.00	94.09	97.45
	4	99.64	99.32	99.48	0.68	4	81.50	89.91	99.32
	5	99.32	99.91	99.61	0.68	5	94.50	94.00	99.32
	6	99.81	96.36	98.06	3.64	6	84.50	93.82	96.36
	7	98.55	98.82	98.68	1.45	7	89.50	90.18	98.55
	8	98.32	98.41	98.36	1.68	8	92.50	99.00	98.32
	9	98.96	99.91	99.43	1.05	9	91.00	93.36	98.95
	All	98.77	98.77	98.77	1.23	All	90.35	93.12	98.77

TABLE III :

 III Results of different end-to-end approaches.

	type of encoder	#params in the network	F1 %error
	Bidirectionnel LSTM 2*50	31.560	98.77	1.23
	Bidirectionnel GRU 2*50	25.060	98.63	1.37
	Forward GRU 100	40.060	97.26	2.74
	Backward GRU 100	40.060	98.85	1.15
	Forward LSTM 100	51.560	97.41	2.59
	Backward LSTM 100	51.560	98.33	1.67

978-1-5386-4543-7/18/$31.00 ©2018 IEEE

ACKNOWLEDGMENTS

The authors express their gratitude to the dedicated personnel who made the Arabic digits dataset used in this study freely available. They are also grateful for the support of NVIDIA Corporation with the donation of the GTX Titan X GPU used in this research work.


Jakub Waszczuk
email: waszczuk@phil.hhu.de

Witold Kieraś
email: wkieras@ipipan.waw.pl

Marcin Woliński
email: wolinski@ipipan.waw.pl

Morphosyntactic disambiguation and segmentation for historical Polish with graph-based conditional random fields

Keywords: word segmentation, morphosyntactic tagging, historical Polish, conditional random fields

published or not. The documents may come

Introduction and related work

Despite the arguments raised in favor of performing end-to-end evaluation of Polish taggers rather than evaluating their disambiguation components only [START_REF] Radziszewski | Taggers gonna tag: An argument against evaluating disambiguation capacities of morphosyntactic taggers[END_REF], the problem of word-level segmentation in Polish received little attention to this day. This is clearly due to relatively low frequency of segmentation ambiguities in Polish and, consequently, low influence of the phenomenon on tagging accuracy.

Several techniques of morphosyntactic tagging for Polish have been explored over the years, including trigrams [START_REF] Dębowski | Trigram morphosyntactic tagger for Polish[END_REF], transformation-based methods 3 (TaKIPI [START_REF] Piasecki | Multiclassifier approach to tagging of Polish[END_REF]; Pantera [START_REF] Acedański | A morphosyntactic Brill tagger for inflectional languages[END_REF]), conditional random fields (WCRFT [START_REF] Radziszewski | A tiered CRF tagger for Polish[END_REF]; Concraft [START_REF] Waszczuk | Harnessing the CRF Complexity with Domain-Specific Constraints. The Case of Morphosyntactic Tagging of a Highly Inflected Language[END_REF]), and neural networks (Toygger [START_REF] Krasnowska-Kieraś | Morphosyntactic disambiguation for Polish with bi-LSTM neural networks[END_REF]; KRNNT [START_REF] Wróbel | KRNNT : Polish recurrent neural network tagger[END_REF]; MorphoDiTa-pl [START_REF] Walentynowicz | MorphoDiTa-based tagger for Polish language[END_REF]). The latter now obtain state-of-the-art results 4 in the task of morphosyntactic tagging for Polish [START_REF] Kobyliński | Results of the PolEval 2017 competition: Partof-speech tagging shared task[END_REF]. All these taggers adopt a pipeline architecture, where morphosyntactic disambiguation (including guessing) is preceded by sentence segmentation, word segmentation, and morphosyntactic analysis (not necessarily in this order). 5For instance, WMBT, WCRFT, Concraft, and KRNNT all relegate the three "subsidiary" preprocessing tasks to Maca [START_REF] Radziszewski | Maca -a configurable tool to integrate Polish morphological data[END_REF]. For word segmentation, Maca relies on ad-hoc conversion rules, which transform and simplify the graph. If segmentation ambiguities persist, simple heuristics -e.g. choosing the shortest path among the remaining segmentation paths -are employed in the end. Another solution is used in MorphoDiTa-pl, which encodes all segmentation ambiguities as morphosyntactic ambiguities. More precisely, it relies on an expanded tagset and conversion routines which allow to encode a given segmentation DAG as a sequence over the expanded tagset. Other Polish taggers seem to neglect the problem of ambiguous segmentation altogether. Toygger, for instance, simply requires that the input text is already segmented and analyzed.

The issue with the existing solutions for Polish is that they assume that word segmentation is performed in preprocessing to morphosyntactic disambiguation. However, neither ad-hoc conversion rules nor simple heuristics are sufficient to deal with segmentation ambiguities, as the latter can require contextual information to be correctly dealt with. The method used in MorphoDiTa-pl actually avoids this pitfall to a certain extent, since it represents segmentation ambiguities in terms of morphosyntactic ambiguities. However, it relies on rather ad-hoc conversion routines which do not seem easily generalizable. One might want to enrich segmentation graphs to account for spelling errors, or to represent several segmentation hypotheses arising in a speech processing system, and it is hard to imagine how conversion routines could account for that.

The problem of word segmentation naturally received more attention for languages where it is more prevalent, such as Chinese or Japanese. Within the context of Chinese, segmentation is often regarded as a labeling task over sequences, where one of two labels -Start or NonStart -is assigned to each character in the sequence. CRFs, neural networks, and other labeling methods can be then used to discriminate between the possible Start/NonStart sequences for a given sentence, each sequence uniquely representing the corresponding segmentation [START_REF] Peng | Chinese segmentation and new word detection using conditional random fields[END_REF][START_REF] Chen | Long short-term memory neural networks for Chinese word segmentation[END_REF]. The idea of modeling morphological segmentation graphs directly with CRFs was proposed by [START_REF] Kudo | Applying Conditional Random Fields to Japanese Morphological Analysis[END_REF] for Japanese, where a DAG-based CRF assigns a probability to each path in a given segmentation DAG, thus allowing to discriminate between different segmentations and the corresponding morphological descriptions at the same time.

In this work, we use a method similar to [START_REF] Kudo | Applying Conditional Random Fields to Japanese Morphological Analysis[END_REF] and apply it to Polish by extending an existing CRF-based tagger, Concraft, to handle ambiguous segmentation graphs (see Sec. 3). The system is coupled with Morfeusz [START_REF] Woliński | Morfeusz reloaded[END_REF], a morphosyntactic analyzer for Polish, which represents both morphosyntactic and segmentation ambiguities in the form of a DAG (see Sec. 2). Finally, we evaluate our system on historical Polish, where segmentation ambiguities are more prominent than in the contemporary language, and show that our system significantly outperforms several baseline segmentation methods (see Sec. 4).

Morfeusz

Similarly to other systems listed in Section 1, we assume that morphological disambiguation is preceded by dictionary lookup providing all possible interpretations of the input text. This task is performed by the morphological analyser Morfeusz 2 [START_REF] Woliński | Morfeusz reloaded[END_REF], which is well suited to processing historical texts. Namely, Morfeusz allows to customize all linguistically sensitive parts of the analysis: inflectional dictionary, rules of segmentation and the tagset. Appropriate adaptation of Morfeusz to 19 th century and Baroque Polish was done by the authors of the corpora we use, see [START_REF] Kieraś | Morphosyntactic Annotation of Historical Texts. The Making of the Baroque Corpus of Polish[END_REF] and [START_REF] Kieraś | Manually Annotated Corpus of Polish Texts Published between 1830 and 1918[END_REF].

Morfeusz accepts the text as a stream of characters, which it splits into tokens and describes each of them as an inflectional form by assigning a lemma and a morphosyntactic tag containing grammatical features of the form, starting with the part of speech. The tokens generated by Morfeusz are words or parts of words (they do not contain spaces). Segmentation in Morfeusz may be ambiguous. For that reason Morfeusz does not represent its output as a flat list, but as a DAG (directed acyclic graph) of morphological interpretations of tokens.

The past tense of Polish verbs has two variants, e.g. czytałem and (e)m czytał (1st person singular of 'to read'). The latter variant is interpreted by Morfeusz as consisting of two separate inflectional forms, (e)m being an auxiliary form of the verb być 'to be', which is written together with a preceding token. This variant of past tense was readily used in historical Polish, while in the contemporary language it is present only in specific constructions. The auxiliary form takes part in systematic homonymy with historical forms of numerous adjectives ending in -em, e.g. waszem ('yours' in instrumental or locative case of masculine or neuter gender). This word may be interpreted in ambiguous ways represented by the graph shown in Figure 1. The first token on each path is a form of the adjective wasz 'your' in various cases and genders (denoted with simplified Morfeusz tags). The second token is the auxiliary form of the verb być 'to be' used by the past tense. Depending on the context, each of the three alternative segmentation paths may constitute the correct interpretation. Historical Polish provides also examples of accidental ambiguities in segmentation, e.g. the word potym can be interpreted as the preposition po 'after' written together with the form tym of the pronoun to 'that' or as the form poty of the noun pot 'sweat' and an auxiliary m.

Graph-based CRFs

A sequential CRF [START_REF] Sutton | An introduction to conditional random fields[END_REF] defines the conditional probability of a sequence of labels y ∈ Y n given a sentence x ∈ X n of length n as:

p θ (y|x) = Φ θ (y, x) Z θ (x) with Z θ (x) = y ∈Y n Φ θ (y , x) (1)
Intuitively, the potential function Φ θ (y, x) represents the plausibility of sequence of labels y given sentence x -the higher Φ θ (y, x) is, the more probable y w.r.t. x is -while the normalization factor Z θ (x) ensures that the probabilities of the individual label sequences sum up to 1. In the particular case of 1-order sequential CRFs, the potential is defined as:

Φ θ (y, x) = exp i=1..n k θ k f k (y i-1 , y i , x) , ( 2
)
where θ is a parameter vector and f k (y i-1 , y i , x) is a binary feature function determining if the k-th feature holds within the context of (y i-1 , y i , x). 6 Defining the exact form of feature functions is a part of the feature engineering process and will depend on the particular application. In our experiments (see Sec. 4), we relied on the Concraft's default feature templates. Constrained CRFs. Concraft relies on a constrained version of sequential CRFs, in which to each position i in the input sequence a set of possible labels r i ⊆ Y is assigned 7 . When the sets of the potential morphosyntactic interpretations of the individual words in the sentence are available, such position-wise constraints can be successfully applied to both speed up processing and improve the tagging accuracy [START_REF] Waszczuk | Harnessing the CRF Complexity with Domain-Specific Constraints. The Case of Morphosyntactic Tagging of a Highly Inflected Language[END_REF]. Formally, for a given sequence y ∈ i r i :

p θ (y|x, r) = Φ θ (y, x) Z θ (x, r) with Z θ (x, r) = y ∈ i ri Φ θ (y , x). ( 3
)
The probability of sequences not respecting the constraints is equal to 0. Note that such sequences are also not accounted for in Z θ (x, r). Constrained DAG-based CRFs. In this work, we rely on a further extension of the constrained model, where the structure of input is a DAG rather than a sequence. Let D = (N D , E D ) be a segmentation DAG of a given sentence, where N D and E D is the set of DAG nodes and edges, respectively. Let also x i ∈ X be the word assigned to i ∈ E D and r i ⊆ Y be the set of i's possible labels. We adapt the model to discriminate between the possible paths y ∈ P (D, r), where P (D, r) denotes the set of labeled paths encoded in D.

p θ (y|x, r, D) = Φ θ (y, x, D) Z θ (x, r, D) with Z θ (x, r, D) = y ∈P (D,r) Φ θ (y , x, D). ( 4
)
The potential, in turn, is defined as:

Φ θ (y, x, D) = exp i∈Dom(y) k θ k f k (y i-1 , y i , x, D) , (5)
where Dom(y) ⊂ E D is the set of edges on the path, y i denotes the label assigned to edge i ∈ E D , and y i-1 denotes the label assigned to the preceding edge. Within the context of morphosyntactic tagging, the above model assigns a probability to each DAG-licensed segmentation of the input sentence with a particular morphosyntactic description assigned to each segment on the path. Hence, maximizing p θ (y|x, r, D) over all the labeled paths in D jointly performs segmentation and disambiguation, as desired.

Inference. The standard algorithms for sequential CRFs can be straightforwardly adapted to DAG-based CRFs. This includes the max-product algorithm used for Viterbi decoding (i.e., finding the most probable labeled path for a given DAG and constraints) and sum-product algorithm used for computing the forward and backward sums [START_REF] Wainwright | Graphical models, exponential families, and variational inference[END_REF]. These two algorithms, in turn, allow to compute the posterior marginal probabilities of the individual segments and labels in the graph, the expected counts of CRF features per sentence, and to perform the maximum likelihood-based parameter estimation process, neither of which is particularly dependent on the underlying structure (sequence vs. DAG). We refer interested readers to [START_REF] Kudo | Applying Conditional Random Fields to Japanese Morphological Analysis[END_REF] for more information on extending CRFs to DAGs.

Observations. Concraft relies on two types of features: 2-order transition features (t i-2 , t i-1 , t i ), and observation features (o i , t i ), where o i is an observation (wordform, suffix, prefix, shape, etc.) related to word i. Observations can include information about the preceding and following words -e.g., "the wordform of the segment on position i-2" -straightforward to obtain with sequential CRFs. However, in the case of DAGs position i -2 may not be uniquely defined.

To overcome this issue, [START_REF] Kudo | Applying Conditional Random Fields to Japanese Morphological Analysis[END_REF] limit the scope of features to two adjacent words, directly accessible in their 1-order model. We adopt a different solution, where the predecessor i-1 (successor i+1, respectively) of edge i ∈ E D is defined as the shortest (in terms of wordform length) edge preceding (following, respectively) i. This allows to define observations in terms of words arbitrarily distant from the current edge, which enables us to use Concraft's feature templates. Note that this does not mean that the model will prefer shorter paths, it simply means that observations are defined at a lower level of granularity. We believe this approach to be reasonable, as long as it is consistently used for both training and tagging.

Experimental evaluation

Dataset. Our dataset consists of two separate gold-standard historical corpora of Polish. The first is a manually annotated subcorpus of the Baroque Corpus of frequency-based method suffers from the length bias problem, as revealed by the differences between its precision and recall.

Conclusions and future work

The existing taggers for Polish either neglect the problem of ambiguous segmentation, or adopt ad-hoc approaches to solve it. By extending an existing CRF-based tagger for Polish, Concraft, to work directly on segmentation graphs provided by Morfeusz, we designed a system which addresses this deficiency by performing disambiguation and word-level segmentation jointly. Evaluation of our system on two historical datasets, both containing a non-negligible amount of segmentation ambiguities, showed that it significantly outperforms several baseline segmentation methods, including a frequency-based method.

The advantages of neural methods, now state-of-the-art in the domain, over CRFs include their ability to capture long-distance dependencies and to incorporate dense vector representations of words. For future work, we would like to explore the possibility of alleviating these weaknesses of CRFs, and the possibility of adapting neural methods to DAG-based ambiguity graphs. Following our claim that contextual information is required to properly deal with segmentation ambiguities, it seems clear that the principal way of improving segmentation accuracy is to focus on the quality of the subsequent NLP modules -disambiguation, parsing -as long as they are able to handle ambiguous segmentations.

Fig. 1 .

 1 Fig. 1. Ambiguous segmentation of the word waszem

Based on algorithms involving automatic extraction of rules.

See: http://poleval.pl/index.php/results/

By extension, this holds true also for ensemble taggers, e.g. PoliTa[START_REF] Kobyliński | PoliTa: A multitagger for Polish[END_REF].

Intuitively, f k has a positive influence on the modeled probability if θ k > 0, negative influence if θ k < 0, and no influence whatsoever if θ k = 0.

With ri = Y for out-of-vocabulary words.

Note that these results abstract from the potential morphosyntactic analysis errors.

Acknowledgements

The work being reported was partially supported by a National Science Centre, Poland grant DEC-2014/15/B/HS2/03119.

The tagset of the 1830-1918 corpus consists of 1449 possible tags, from which 1292 were chosen at least once by human annotators. The Baroque tagset is much larger: it consists of 2212 possible tags and 1940 of them were used by annotators. The size of the Baroque tagset reflects the extensive time span covered by the corpus as well as significant grammatical changes which took place in that period, such as the grammaticalisation of masculine personal gender. It is assumed that since the turn of the 18 th and 19 th centuries Polish morphosyntactic system was not subject to major changes.

Evaluation. The results of 10-fold cross-validation of our system on both historical corpora are presented in Tab. 1. We measured the quality of morphosyntactic tagging 8 and segmentation in terms of precision and recall. If several tags were assigned to a segment in gold data, we considered the choice of our system as correct if it belonged to this set. In case of segmentation, the choices of morphosyntactic tags were not accounted for. We compared our system with three baseline segmentation methods. The first and the second one systematically chooses the shortest and the longest possible segmentation path, respectively. The third system is based on frequencies with which ambiguous segments are marked as chosen in gold data. Namely, we define the probability p(x) of a segment x as #(x chosen in gold + 1)/#(x present in gold + 2), 9 and the probability of a given segmentation path as a product of the probabilities of its component segments. Our system outperforms all three baseline methods significantly. Best among the baselines, the


Emna Bel Kamel
email: emna.belkamel@telecom-bretagne.eu

Alain Peden

Patrice Pajusco

RCS Modeling and Measurements for Automotive Radar Applications in the W Band

Keywords: millimeter wave, measurement, RCS. I

This paper describes a reliable methodology for radar cross section (RCS) measurement of complex small and large targets in the W band. The backscattering behavior of a small car model was measured in an anechoic chamber along with various automotive related targets in a wide gymnasium. Experimental performance in the anechoic chamber is compared to the simulation results. Our simulation model is based on deterministic scattering centers, determined by high frequency approaches, like the physical optics (PO) and the physical theory of diffraction (PTD). Nevertheless, simulations of realistic large objects are both time consuming and difficult to implement. The proposed measurement configuration enables the extraction of non-predetermined scattering points for large object modeling which will significantly decrease the simulation time for road scenarios in radar applications.

INTRODUCTION

In the ongoing effort to improve road safety and communication systems between vehicles, robust RCS signatures of automotive targets are crucial. Generating the cartography of a real radar environment is required to design efficient radar systems. Our study is focused on automotive complex radar target modeling and experimental characterization in order to optimize detection systems. The complexity of targets originates from complex shapes as well as various electromagnetic phenomena. This involves simple and multiple reflections, edge and corner diffraction and creeping waves. In the millimeter wave bands, the modeling of electrically large problems by rigorous electromagnetic methods, such as the method of moments (MOM), is a complex task [START_REF] Harrington | The Method of Moments in Electromagnetics[END_REF]. They are computationally too expensive when the target dimension is several orders of wavelength. The direct use of the asymptotic methods is still applicable but requires weeks to run the simulation. This can be resolved by using the scattering points approach. They are representative of the most important geometric parts in terms of scattering. The complexity of the target can then be reduced to limited number of scattering points. Various techniques for radar target scattering point extraction have been presented in the literature [START_REF] Wang | A deterministic method for generating a scattering-center model to reconstruct the RCS pattern of complex radar targets[END_REF]- [START_REF] Schuler | Extraction of Virtual Scattering Centers of Vehicles by Ray-Tracing Simulations[END_REF]. [START_REF] Wang | A deterministic method for generating a scattering-center model to reconstruct the RCS pattern of complex radar targets[END_REF], [START_REF] Bhalla | 3D scattering center representation of complex targets using the shooting and bouncing ray technique: a review[END_REF], [START_REF] Schuler | Extraction of Virtual Scattering Centers of Vehicles by Ray-Tracing Simulations[END_REF].

A few studies regarding three-dimensional (3D) complex target modeling in terms of simple and multiple reflection and edge diffraction in the W band can be found [START_REF] Chen | Numerical study of pedestrian RCS at 76-77 GHz[END_REF]- [START_REF] Treml | Radar cross section prediction and measurement at 77 GHz[END_REF]. Indeed, simulation of radar signatures is a challenging task, due to the lack of available high-fidelity electromagnetic models of complex objects such as vehicles. Running these models in the W band (76 to 81 GHz for automotive radar) also requires a fairly large memory and computation time, due to the large size of the target in terms of wavelength. Moreover, RCS experiments in an anechoic chamber give accurate results but can be very expensive, depending on the size of the targets. The platform used to hold large objects like vehicles is available in very few research laboratories in the world. Only a limited set of reports on RCS simulation and measurements of vehicles and pedestrians in the W band have been reported to date [START_REF] Geary | Automotive radar target characterization from 22 to 29 GHz and 76 to 81 GHz[END_REF]- [START_REF] Andres | 3D detection of automobile scattering centers using UWB radar sensors at 24/77 GHz[END_REF]. The main focus of this work is to investigate car backscattering behavior in millimeter wave bands through simulation and measurements. This paper is organized as follows. Section II describes the RCS model that includes the shadowing and multiplescattering effects. The methodology used to optimize the simulation procedure is also discussed in terms of car model simplification and numerical methods. Section III describes the RCS measurement system associated with an anechoic chamber and a gymnasium. The experimental results are also presented here. Finally conclusions and perspectives are drawn in Section IV.

II. RCS SIMULATION OF COMPLEX TARGETS

A. Deterministic scattering center model

Our Matlab model accounts for shadowing effects by performing a visibility test. The target is discretized into triangular facets to achieve an accurately estimated scattering contribution for each part. Illuminated and shaded surfaces are determined according to the algorithm of Trumbore [START_REF] Albayrak | RCS computations with PO/PTD for conducting and impedance objects modeled as large flat plates[END_REF]. A positive result of the scalar product between the normal to the triangle's surface and the direction of the incident wave indicates that a facet is facing the radar. Then, a Z-buffer approach [START_REF] Albayrak | RCS computations with PO/PTD for conducting and impedance objects modeled as large flat plates[END_REF] is applied to determine the portions of the target that can be hidden by other parts. Depth information is used to eliminate facets that are not visible.

To compute the RCS, the deterministic approach [START_REF] Wang | A deterministic method for generating a scattering-center model to reconstruct the RCS pattern of complex radar targets[END_REF] was used, which requires only modeling target by facets. It is based on a predefined distribution of fixed scatterers. The position of the center of a surface, edge or corner can represent the position of the scattering centers. The amplitude and phase of field originating from a scattering point are computed by asymptotic methods. We then implemented the PO approximation to obtain specular reflection from the target [START_REF] Knott | Radar Cross Section[END_REF]. The scattering points are the centers of the mesh surfaces. The scattered field is computed from the radiation integrals of current densities. In the case of a polygonal planar surface, this integral is solved in a relatively simple way as suggested in [START_REF] Gordon | Far-field approximations to the Kirchoff-Helmholtz representations of scattered fields[END_REF] by transforming the surface integral into a linear integral. Away from the specular directions, the edge effects contribute significantly to the scattered field. The PTD was then developed to overcome the PO limitation, as it takes into account the diffraction by edges. The scattering points are the centers of the edges. The source of the diffracted field is attributed to the fictitious electric and magnetic equivalent currents, along the edge. The expressions given by Michaeli [START_REF] Michaeli | Equivalent edge currents for arbitrary aspects of observation[END_REF], based on the fringe currents existing on the edge, are used. These currents are calculated assuming the straight edge locally around each point of the contour being considered.

B. Simulated car model generation

The short wavelength in the W band requires very fine meshes to model a target, hence posing extensive computational overhead (memory and time). At high frequencies, the RCS predictions from coarse mesh become inaccurate. Coarse mesh will not reflect all the details of the target. Indeed, the degree of error is greatly geometrydependent. The facetization representation of the model and the electromagnetic simulation approach are two key aspects of RCS computation accuracy. The vehicle model used in our RCS simulator describes essentially its exterior metallic structure. It is represented by flat triangular facets. The model usually needs to be corrected for overlapped meshes before running the EM simulation. We chose to apply a surface mesh simplification to the car model in order to reduce its complexity and storage requirements, while perceiving the overall shape, volume and boundaries as much as possible. Curved features are modeled well with small flat triangular facets. Thus, discretization error can be minimized to a large degree. Tests demonstrate that the total number of triangular meshes can be reduced by a factor of 2 with MeshLab software.

C. Simulation results

In order to describe the performance of our model in terms of CPU time and memory, we first simulated a generic model of a small, 'car-like' object (6.6 × 3.6 × 4.3 cm 3 ), shown in Fig. 1 (a). The results were further compared with the Multilevel Fast Multipole Method (MLFMM) in FEKO, considering it as a reference method. The size of the model was selected due to the memory limitations when simulating a car having a realistic size at 77 GHz with MLFMM in FEKO. 203044 mesh triangles are needed to simulate the mini car model with MLFMM, and only 30 triangles with our Matlab code. This coarse mesh is justified by the 16 λ criterion [START_REF] Song | Facet noise in RCS prediction[END_REF]. The RCS simulated by our model is in accordance with the MLFMM, as it will be shown in section III. It also reduces requirements in terms of computational resources. The time resource varies from 384 seconds for our model compared to approximately 4-5 days with the MLFMM. The model is then applied to a simplified pseudorealistic car model (Fig. 1 (b)), and compared to FEKO using the large physical optic (LE-PO) solution. The MLFMM method cannot be applied to this car model as it is electrically very large. An excellent agreement can be seen in Fig. 2 between the results obtained by each of PO, PTD and LE-PO solutions. Nevertheless, the deterministic scattering center model developed in Matlab is not yet optimized. It took approximately five days using 7962 meshes, due to the large number of deterministic scattering points required. Thus, the RCS model should be enhanced by RCS measurements of real automotive targets. High frequency asymptotic methods can be applied to scattering centers extracted from experiments, instead of deterministic ones. The number of scattering points is reduced, as is the computation time.

III. MEASUREMENT SETUP AND RESULTS

A. Anechoic chamber measurements

In order to validate the RCS model, an automated characterization bench for RCS was developed. It is composed of a vector network analyzer (VNA) from Rohde & Schwarz (ZVT20), installed outside the anechoic chamber. It is attached to millimeter wave heads to extend the measurement to the 72-110 GHz band. Due to the difficulty involved in placing the VNA and the millimeter heads in the anechoic chamber (3 × 1.12 × 1 m 3 ), they were installed outside it with its door kept open during measurements. A quasi monostatic configuration was used with two horn antennas, operating in the 72-110 GHz range. The measured horn antenna gain is about 23.5 dBi. The azimuth and elevation beamwidth angles are about 26° and 22° It is worth mentioning that the stand foam should be taken into consideration, because its scattering contribution becomes important compared to the low target scattering at millimeter bands, and can potentially influence the measurement results. Therefore, an extra tilted absorber was placed in front of the foam stand and its position and angle were optimized during a series of preliminary tests in order to minimize the stand reflections.

The measurements were carried out in three steps. The backscattered signal from the target was first measured. Secondly, a reference target measurement with known RCS was performed to eliminate dependence on the measurement system. The calibration object chosen here was a 6 cm diameter sphere since its RCS can be determined analytically and it does not require angular alignment. It was followed by an empty room measurement, made under exactly similar conditions as the RCS measurements but without any target present on the stand. All frequency-dependent measurements were transformed in the time domain by an inverse Fourier transform. These impulse responses were subtracted from an empty room measurement to reduce unwanted echoes. A time gating centered on the object response followed by an FFT refined the frequency domain response and thus the RCS. The resulting signal was then divided by the 6 cm metallic sphere. This indirect calibration [START_REF] Wiesbeck | Single reference, three target calibration and error correction for monostatic, polarimetric free space measurements[END_REF] was chosen for all the measurements presented here because of the improved accuracy compared to direct calibration. As only a vertical polarization was used in all measurements, a single reference target at this polarization was needed. Nevertheless, measurements using horizontal polarization or crosspolarization configuration can be performed with the test bench.

After validating the RCS measurement setup, a campaign of angle and frequency RCS measurements was carried out on a series of canonical and complex objects. Figure 4 gives the comparison of RCS distribution measurement results from -180° to 180° of a car-like object in the 72-110 GHz frequency band. This object is made from ROHACELL foam and painted with liquid silver. Despite its relatively simple geometry, it includes several scattering mechanisms, especially edge and corner diffractions. Measurement results matched well with simulations obtained by our PTD Matlab code and by MLFMM simulated using FEKO. The deviation at -90° between simulation and measurement occurs from tolerances between the simulated model and the real measured one. These experiments validate the setup and calibration procedure.

B. Measurements in gymnasium

To characterize the RCS of large objects such as cars, indoor measurements were taken in a large gymnasium. The measurement setup used for the radar cross section measurement is described in section A. The target was placed in the center of the gymnasium, at a distance of 12 m from the horn antennas. Measurement techniques for large targets generally require measuring the RCS in azimuth by rotating the target on a turntable. In our measurement methodology, shown in Fig. 5, a turntable was not needed. Thus, the radar measurement system was driven manually along a circle of 12 m radius, while the target was fixed. The circle was discretised from ϕ = 0° to 180° (assuming the symmetry of the target) with a 2° angular step. The RCS data was taken at each aspect angle and over the 76-81 GHz bandwidth. No radar-absorbing material was placed around the car during the measurements. Therefore, the resulting backscattered signal contained the target response as well as unwanted contributions from ground and gym components. Direct calibration using the radar equation was applied here due to its simplicity (no reference target is required). The time gating is also of utmost importance to reduce multipath components. The characterization of various cars such as a sedan (Peugeot 406 and Giulietta), a small sedan (Peugeot 107), a van (Renault Trafic), a small van (Peugeot 5008), and other targets (bicycle and pedestrian), was performed. All the measurements were conducted in VV configuration. The impulse response deduced from the large band measurement contains maxima in the car range, corresponding to its scattering points. The monostatic RCS of the car is the sum of the contribution from all these scattering centers. Fig. 6 represents the RCS of the cited vehicles. We note a similar behavior of the RCS pattern for all the measured cars. The RCS of the flat part ( 0, 90, 180 ϕ =  ) is significantly larger than that of the corner part ( 45, 135 ϕ =  ). Moreover, the RCS maximum values vary between 8.7 -20.5 dBm 2 for the front of the cars, 14.4 -24.6 dBm 2 for the back and 19 -22 dBm 2 for the side. These values are in accordance with the results presented in [START_REF] Geary | Automotive radar target characterization from 22 to 29 GHz and 76 to 81 GHz[END_REF]- [START_REF] Matsunami | RCS measurements for vehicles and pedestrian at 26 and 79GHz[END_REF]. We note that the simulated RCS maximum value for a typical vehicle, presented in Fig. 2, is higher than the measured one, as the wheels are not taken into account in the simulation. Figure 7 shows the RCS of a bicycle and a pedestrian. As expected, both RCS values are smaller than those of cars because there are few or no flat or corner metallic parts. The RCS maximum value of the bicycle is 10 dBm 2 in the side view but very weak in the front and back view. The RCS mean value of a pedestrian is around -3 dBm 2 . In particular, the pedestrian scattering is similar to a cylinder which has an almost fixed RCS pattern for azimuth aspect angles.  , corresponding respectively to the front, side and back of the car. For the bicycle and pedestrian, there is a roughly uniform distribution of scattering centers. A simple algorithm based on a scattering point approach was implemented to get radar images presented in Fig. 9. The obtained images give a half-top view of the target, highlighting the major scattering points that can be used to reconstruct its RCS. In this work, a first-order approximation of the RCS of a car in the W band has been presented by using highfrequency asymptotic methods, while reducing complexity. Accurate measurement results have been obtained for various automotive targets using a cost-effective measurement setup. It was found that the maximum value of RCS for the measured vehicles varies between 19 and 25 dBm 2 . The maximum value of RCS for the bicycle was found to be 10 dBm 2 , compared to the mean value around -3 dBm 2 for a pedestrian. For future perspective, the use of scattering centers extracted by measurement can drastically reduce target model complexity. This can significantly reduce the simulation time for road scenarios in radar applications.

Fig. 1 .Fig. 2 .

 12 Fig. 1. (a) Car_like model, (b) Sedan car model meshed with 7962 facets

  is sufficient to ensure a uniform illumination of the target, as shown in Fig.3. The target was placed inside the anechoic chamber. It was mounted on a foam stand placed on a stepper motor at a 2.6 m distance from the antennas, ensuring the farfield condition. The 38 GHz bandwidth for the RCS measurement band provided a high range resolution of 4 mm. The whole bandwidth was swept across 2048 points to consider both the target response and echoes in the time domain. It gives an unambiguous range of about 61 m. Measurements were fully automated by remote control of the rotation of the stand and the VNA. The complex S 21 parameter was recorded from the VNA over the entire bandwidth, at each angle of given measurement scenario.

Fig. 3 .Fig. 4 .

 34 Fig. 3. (a) Millimeter wave measurement configuration, (b) VNA converters in the W-band with rectangular horns, (c) car-like object in foam material (size : 6.6 × 3.6 × 4.3 cm3)

Fig. 5 .

 5 Fig. 5. a) Millimeter-wave measurement configuration , b) measurement environment of the gymnasium

Fig. 6 .Fig. 7 .

 67 Fig. 6. RCS of five respresentative cars, front view

Fig. 8 .Fig. 9 .

 89 Fig. 8. Range-azimuth plots for three representative targets (car, bicycle, pedestrian)