# Scientific Production

## SEARCH

Search## Journal Paper

### On Nested Property of Root-LDPC Codes

IEEE Wireless Communications Letters, Early Access, p.1, 2021.

We investigate on binary Protograph Root-LDPC codes that can embed an interesting property, called nested property. This property refers to the ability for a coding scheme to achieve full diversity and equal coding gain for any number of received coded blocks and for any configuration of the received code blocks. One can take advantage of this property for “carousel”-type transmissions broadcasting cyclically coded information. For regular Root-LDPC codes, we show that these codes inherently have both properties over the nonergodic block fading channel and when message passing decoding is used. Then, we show that irregular Root-LDPC structures could not provide equal coding gain except if explicit design rules are enforced to ensure that the nested property is fulfilled when designing irregular Root-LDPC codes. Simulation results show that designed nested Root-LDPC codes achieve good performance and full diversity for coding rates R=1/2, R=1/3 and R=1/4.

Digital communications / Localization and navigation and Space communication systems

READ MORE### Cramér-Rao Bound for a Mixture of Real -and Integer - Valued Parameter Vectors and its Application to the Linear Regression Model

Elsevier, vol. 179, February, 2021.

Performance lower bounds are known to be a fundamental design tool in parametric estimation theory. A plethora of deterministic bounds exist in the literature, ranging from the general Barankin bound to the well-known Cramér-Rao bound (CRB), the latter providing the optimal mean square error performance of locally unbiased estimators. In this contribution, we are interested in the estimation of mixed real- and integer-valued parameter vectors. We propose a closed-form lower bound expression leveraging on the general CRB formulation, being the limiting form of the McAulay-Seidman bound. Such formulation is the key point to take into account integer-valued parameters. As a particular case of the general form, we provide closed-form expressions for the Gaussian observation model. One noteworthy point is the assessment of the asymptotic efficiency of the maximum likelihood estimator for a linear regression model with mixed parameter vectors and known noise covariance matrix, thus complementing the rather rich literature on that topic. A representative carrier-phase based precise positioning example is provided to support the discussion and show the usefulness of the proposed lower bound.

Signal and image processing / Localization and navigation and Space communication systems

READ MORE## PhD Thesis

### Hybridation GNSS/5G pour la navigation en milieu urbain

Defended on February 25, 2021.

Over the past few years, the need for positioning, and thus the number of positioning services in general, has been in constant growth. This need for positioning has been increasingly focused on constrained environments, such as urban or indoor environments, where GNSS (Global Navigation Satellite System) is known to have significant limitations: multipath as well as the lack of Line-of-Sight (LOS) satellite visibility degrades the GNSS positioning solution and makes it unsuitable for some urban or indoor applications. In order to improve the GNSS positioning performance in constrained environments, many solutions are already available: hybridization with additional sensors, [1], [2] or the use of signals of opportunity (SoO) for example, [3], [4], [5], [6], [7], [8]. Concerning SoO, mobile communication signals, such as the 4G Long Term Evolution (LTE) or 5G, are naturally envisioned for positioning, [3], [9], [10]. Indeed, a significant number of users are expected to be “connected-users” and 5G systems offers promising opportunities. 5G technology is being standardized at 3GPP [11]; the first complete release of 5G specifications, Release-15, was provided to the community in March 2018. 5G is an emerging technology and its positioning performance, as well as a potential generic receiver scheme to conduct positioning operations, is still under analysis. In order to study the potential capabilities provided by 5G systems and to develop a 5G-based generic positioning module scheme, the first fundamental step is to develop mathematical models of the processed 5G signals at each stage of the receiver for realistic propagation channel models: the mathematical expression of the useful received 5G signal as well as the AWG (Additive White Gaussian) noise statistics. In the Ph.D., the focus is given to the correlation operation which is the basic function implemented by typical ranging modules for 4G LTE signals [12], DVB signals [7], [8], and GNSS [13]. In fact, the knowledge of the correlation output mathematical model could allow for the development of optimal 5G signal processing techniques for ranging positioning. Previous efforts were made to provide mathematical models of received signals at the different receiver signal processing stages for signals with similar structures to 5G signals – Orthogonal Frequency Division Multiplexing (OFDM) signals as defined in 3GPP standard, [14]. OFDM signal-type correlator output mathematical model and acquisition techniques were derived in [7], [15]. Moreover, in [8], [15], tracking techniques were proposed, analyzed and tested based on the correlator output mathematical model of [7]. However, these models were derived by assuming a constant propagation channel over the duration of the correlation. Unfortunately, when the Channel Impulse Response (CIR) provided by a realistic propagation channel is not considered to be constant over the duration of the correlation, the correlator output mathematical models are slightly different from the mathematical models proposed in [7], [8]. Therefore, the first main point considered in the Ph.D. consists in the development of mathematical models and statistics of processed 5G signals for positioning. In order to derive accurate mathematical models, the time evolution impact of the 5G standard compliant propagation channel is of the utmost importance. Note that, in the Ph.D., the continuous CIR will be approximated by a discretized CIR, and the continuous time-evolution will be replaced by the propagation channel generation sampling rate notion. This approximation makes sense since, in a real transmission/reception chain, the received time-continuous signal is, at the output of the Radio-Frequency (RF) front-end, sampled. Therefore, a preliminary step, prior to derive accurate mathematical models of processed 5G signals, consists in determining the most suitable CIR-generation sampling interval for a selected 5G standard compliant propagation channel, QuaDRiGa: trade-off between having a realistic characterization and its complexity. Complexity is especially important for 5G compliant channels with multiple emitter and receiver antennas, and high number of multipath. Then, the impact of a time-evolving propagation channel inside an OFDM symbol duration is studied. A method to select the most appropriate CIR sampling interval for accurate modelling of symbol demodulation, correlator outputs and delay tracking will also be proposed. Based on the correlator output mathematical models developed for realistic multipath environments for both GNSS and 5G systems, ranging modules are then developed. These ranging modules outputs the pseudo ranging measurements required to develop navigation solution. In order to improve the positioning availability and GNSS positioning performance in urban environment through the exploitation of 5G signals, both systems, GNSS and 5G communication systems, must be optimally combined. In fact, in order to achieve this optimal combination, both types of signals must be optimally processed, and the mathematical model of their generated pseudo range measurements must be accurately characterized. The second main objective of the Ph.D. aims thus at realistically characterizing GNSS and 5G pseudo range measurement mathematical models and at developing hybrid navigation modules exploiting/adapted to the derived pseudo range measurements mathematical models. In order to validate, the mathematical models developed in the Ph.D., a simulator is designed. The pseudo range measurements mathematical models are derived from a realistic simulator which integrates a typical GNSS receiver processing module and a typical 5G signal processing module proposition; moreover, in order to achieve a realistic characterization, the simulator implements highly realistic propagation channels for GNSS, SCHUN [16], and for 5G, QuaDRiGa [17] is developed. The hybrid navigation modules to be implemented and compared in this work are an Extended Kalman Filter (EKF) and an Unscented Kalman Filter (UKF). The performances of these hybrid navigation modules are then studied to quantify the improvements bringing by 5G TOA measurements.

Signal and image processing and Digital communications / Localization and navigation

READ MORE## PhD Defense Slides

### Hybridation GNSS/5G pour la navigation en milieu urbain

Defended on February 25, 2021.

Over the past few years, the need for positioning, and thus the number of positioning services in general, has been in constant growth. This need for positioning has been increasingly focused on constrained environments, such as urban or indoor environments, where GNSS (Global Navigation Satellite System) is known to have significant limitations: multipath as well as the lack of Line-of-Sight (LOS) satellite visibility degrades the GNSS positioning solution and makes it unsuitable for some urban or indoor applications. In order to improve the GNSS positioning performance in constrained environments, many solutions are already available: hybridization with additional sensors, [1], [2] or the use of signals of opportunity (SoO) for example, [3], [4], [5], [6], [7], [8]. Concerning SoO, mobile communication signals, such as the 4G Long Term Evolution (LTE) or 5G, are naturally envisioned for positioning, [3], [9], [10]. Indeed, a significant number of users are expected to be “connected-users” and 5G systems offers promising opportunities. 5G technology is being standardized at 3GPP [11]; the first complete release of 5G specifications, Release-15, was provided to the community in March 2018. 5G is an emerging technology and its positioning performance, as well as a potential generic receiver scheme to conduct positioning operations, is still under analysis. In order to study the potential capabilities provided by 5G systems and to develop a 5G-based generic positioning module scheme, the first fundamental step is to develop mathematical models of the processed 5G signals at each stage of the receiver for realistic propagation channel models: the mathematical expression of the useful received 5G signal as well as the AWG (Additive White Gaussian) noise statistics. In the Ph.D., the focus is given to the correlation operation which is the basic function implemented by typical ranging modules for 4G LTE signals [12], DVB signals [7], [8], and GNSS [13]. In fact, the knowledge of the correlation output mathematical model could allow for the development of optimal 5G signal processing techniques for ranging positioning. Previous efforts were made to provide mathematical models of received signals at the different receiver signal processing stages for signals with similar structures to 5G signals – Orthogonal Frequency Division Multiplexing (OFDM) signals as defined in 3GPP standard, [14]. OFDM signal-type correlator output mathematical model and acquisition techniques were derived in [7], [15]. Moreover, in [8], [15], tracking techniques were proposed, analyzed and tested based on the correlator output mathematical model of [7]. However, these models were derived by assuming a constant propagation channel over the duration of the correlation. Unfortunately, when the Channel Impulse Response (CIR) provided by a realistic propagation channel is not considered to be constant over the duration of the correlation, the correlator output mathematical models are slightly different from the mathematical models proposed in [7], [8]. Therefore, the first main point considered in the Ph.D. consists in the development of mathematical models and statistics of processed 5G signals for positioning. In order to derive accurate mathematical models, the time evolution impact of the 5G standard compliant propagation channel is of the utmost importance. Note that, in the Ph.D., the continuous CIR will be approximated by a discretized CIR, and the continuous time-evolution will be replaced by the propagation channel generation sampling rate notion. This approximation makes sense since, in a real transmission/reception chain, the received time-continuous signal is, at the output of the Radio-Frequency (RF) front-end, sampled. Therefore, a preliminary step, prior to derive accurate mathematical models of processed 5G signals, consists in determining the most suitable CIR-generation sampling interval for a selected 5G standard compliant propagation channel, QuaDRiGa: trade-off between having a realistic characterization and its complexity. Complexity is especially important for 5G compliant channels with multiple emitter and receiver antennas, and high number of multipath. Then, the impact of a time-evolving propagation channel inside an OFDM symbol duration is studied. A method to select the most appropriate CIR sampling interval for accurate modelling of symbol demodulation, correlator outputs and delay tracking will also be proposed. Based on the correlator output mathematical models developed for realistic multipath environments for both GNSS and 5G systems, ranging modules are then developed. These ranging modules outputs the pseudo ranging measurements required to develop navigation solution. In order to improve the positioning availability and GNSS positioning performance in urban environment through the exploitation of 5G signals, both systems, GNSS and 5G communication systems, must be optimally combined. In fact, in order to achieve this optimal combination, both types of signals must be optimally processed, and the mathematical model of their generated pseudo range measurements must be accurately characterized. The second main objective of the Ph.D. aims thus at realistically characterizing GNSS and 5G pseudo range measurement mathematical models and at developing hybrid navigation modules exploiting/adapted to the derived pseudo range measurements mathematical models. In order to validate, the mathematical models developed in the Ph.D., a simulator is designed. The pseudo range measurements mathematical models are derived from a realistic simulator which integrates a typical GNSS receiver processing module and a typical 5G signal processing module proposition; moreover, in order to achieve a realistic characterization, the simulator implements highly realistic propagation channels for GNSS, SCHUN [16], and for 5G, QuaDRiGa [17] is developed. The hybrid navigation modules to be implemented and compared in this work are an Extended Kalman Filter (EKF) and an Unscented Kalman Filter (UKF). The performances of these hybrid navigation modules are then studied to quantify the improvements bringing by 5G TOA measurements.

Digital communications / Localization and navigation

READ MORE## Talk

### Robust Standalone GNSS Navigation

Seminar of TéSA, Toulouse, February 24, 2021.

Precise and reliable positioning is nowadays of paramount importance in several mass-market civil, industrial and transport applications, safety-critical receivers and a plethora of engineering fields. In general, Global Navigation Satellite Systems (GNSS) is the positioning technology of choice, but these systems were originally designed to operate under clear skies and its performance clearly degrades under non-nominal conditions. In general, the channel conditions and the main impairments at the receiver level are application dependent. Some harsh propagation conditions, and some relevant applications such as i) urban environments, where a clear impact for autonomous cars and vulnerable road users, the main impairments are multipath, Non-Line-of-Sight (NLOS), shadowing, and a possible lack of satellite visibility in deep urban canyons. ii) For space exploration applications, where a spacecraft is exiting the atmosphere, the main limitations are high receiver dynamics and very weak signal conditions. Such weak signal conditions are mainly due to the use of signals coming from satellites on the opposite side of the Earth (w.r.t. the standard GNSS use). In this talk, we consider the standalone GNSS robust navigation problem, and taking into account the GNSS system-level architecture (space segment, ground segment, user segment), we will talk about the following main signal design challenges: There exist different signals in space, ranging from the legacy GPS L1 C/A Gold codes and BPSK modulation to the Galileo AltBOC signals, each of them having different characteristics, which may have an impact on the achievable PVT performance. Besides the existing signals, and considering the non-nominal conditions of interest, some questions naturally arises: i) which is the best signal (waveform and coding) to improve the mitigation capabilities at the receiver level? ii) each type of impairment requires different signal characteristics or there exists an optimal solution for all of them?

Signal and image processing / Localization and navigation and Space communication systems

READ MORE### Détection d’Anomalies avec Retour Utilisateur

Seminar of TéSA, Toulouse, February 24, 2021.

Quand un satellite fonctionne dans des conditions normales, de nombreux paramètres sont enregistrés et transmis vers un centre de contrôle. Ces paramètres sont analysés régulièrement pour détecter la présence d’éventuelles anomalies. Lorsque ces anomalies sont détectées, le satellite doit les corriger et doit éviter que ces erreurs apparaissent ultérieurement. Dans ce contexte, plusieurs algorithmes de détection d’anomalies ont montré leur intérêt comme l’algorithme LoOP (Local Outlier Probability) qui fournit pour chaque vecteur de données une probabilité d’anomalies ou l’algorithme One-Class SVM qui permet de définir un domaine contenant la majorité des vecteurs de données (supposés normaux) et qui détecte les anomalies situées à l’extérieur de ce domaine. L’objectif de ce séminaire est de proposer des méthodes qui modifient l’algorithme de détection d’anomalies non supervisé à l'aide d'un retour utilisateur afin de diminuer les erreurs de classification (détections manquées/fausses alarmes).

Signal and image processing / Space communication systems

READ MORE## Journal Paper

### Low Complexity Robust Data Demodulation for GNSS

MDPI Sensors, vol. 21, p. 1341, February, 2021.

In this article, we provide closed-form approximations of log-likelihood ratio (LLR) values for direct sequence spread spectrum (DS-SS) systems over three particular scenarios, which are commonly found in the Global Navigation Satellite System (GNSS) environment. Those scenarios are the open sky with smooth variation of the signal-to-noise ratio (SNR), the additive Gaussian interference, and pulsed jamming. In most of the current communications systems, block-wise estimators are considered. However, for some applications such as GNSSs, symbol-wise estimators are available due to the low data rate. Usually, the noise variance is considered either perfectly known or available through symbol-wise estimators, leading to possible mismatched demodulation, which could induce errors in the decoding process. In this contribution, we first derive two closedform expressions for LLRs in additive white Gaussian and Laplacian noise channels, under noise uncertainty, based on conjugate priors. Then, assuming those cases where the statistical knowledge about the estimation error is characterized by a noise variance following an inverse log-normal distribution, we derive the corresponding closed-form LLR approximations. The relevance of the proposed expressions is investigated in the context of the GPS L1C signal where the clock and ephemeris data (CED) are encoded with low-density parity-check (LDPC) codes. Then, the CED is iteratively decoded based on the belief propagation (BP) algorithm. Simulation results show significant frame error rate (FER) improvement compared to classical approaches not accounting for such uncertainty.

Digital communications / Localization and navigation and Space communication systems

READ MORE### Improving the Estimation of the Wavenumber Spectra from Altimeter Observations

IEEE Transactions on Geoscience and Remote Sensing, (under minor revision).

### Reduced-Complexity End-to-End Variational Autoencoder for on Board Satellite Image Compression

Remote sensing, vol. 13, issue 3, p. 447, January, 2021.

Recently, convolutional neural networks have been successfully applied to lossy image compression. End-to-end optimized autoencoders, possibly variational, are able to dramatically outperform traditional transform coding schemes in terms of rate-distortion trade-off; however, this is at the cost of a higher computational complexity. An intensive training step on huge databases allows autoencoders to learn jointly the image representation and its probability distribution, possibly using a non-parametric density model or a hyperprior auxiliary autoencoder to eliminate the need for prior knowledge. However, in the context of on board satellite compression, time and memory complexities are submitted to strong constraints. The aim of this paper is to design a complexity-reduced variational autoencoder in order to meet these constraints while maintaining the performance. Apart from a network dimension reduction that systematically targets each parameter of the analysis and synthesis transforms, we propose a simplified entropy model that preserves the adaptability to the input image. Indeed, a statistical analysis performed on satellite images shows that the Laplacian distribution fits most features of their representation. A complex non parametric distribution fitting or a cumbersome hyperprior auxiliary autoencoder can thus be replaced by a simple parametric estimation. The proposed complexity-reduced autoencoder outperforms the Consultative Committee for Space Data Systems standard (CCSDS 122.0-B) while maintaining a competitive performance, in terms of rate-distortion trade-off, in comparison with the state-of-the-art learned image compression schemes

Signal and image processing / Earth observation

READ MORE## Conference Paper

### Incorporating User Feedback Into One-Class Support Vector Machines for Anomaly Detection

In Proc. 28th European Signal Processing Conference (EUSIPCO), Amsterdam, Netherlands, January 18-22, 2021.

Machine learning and data-driven algorithms have gained a growth of interest during the past decades due to the computation capability of the computers which has increased and the quantity of data available in various domains. One possible application of machine learning is to perform unsupervised anomaly detection. Indeed, among all available data, the anomalies are supposed to be very sparse and the expert might not have the time to label all the data as nominal or not. Many solutions exist to this unsupervised problem, but are known to provide many false alarms, because some scarce nominal modes might not be included in the training dataset and thus will be detected as anomalies. To tackle this issue, we propose to present an existing iterative algorithm, which presents potential anomaly to the expert at each iteration, and compute a new boundary according to this feedback using One Class Support Vector Machine.

Signal and image processing / Space communication systems

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