Ultrasound (US) image restoration from radio frequency (RF) signals is generally addressed by deconvolution techniques mitigating the effect of the system point spread function (PSF). Most of the existing methods estimate the tissue reflectivity function (TRF) from the so-called fundamental US images, based on an image model assuming the linear US wave propagation. However, several human tissues or tissues with contrast agents have a nonlinear behavior when interacting with US waves leading to harmonic images. This work takes this nonlinearity into account in the context of TRF restoration, by considering both fundamental and harmonic RF signals. Starting from two observation models (for the fundamental and harmonic images), TRF estimation is expressed as the minimization of a cost function defined as the sum of two data fidelity terms and one sparsity-based regularization stabilizing the solution. The high attenuation with a depth of harmonic echoes is integrated into the direct model that relates the observed harmonic image to the TRF. The interest of the proposed method is shown through synthetic and in vivo results and compared with other restoration methods.

Low power ultrasonics are used for testing high density polyethylene pipe material. Attenuation and velocity give valuable information on the material in situ and without damages. In this paper we revisit recent data in the frequency band (4,10) megahertz. We prove that propagation is equivalent to random delays following stable probability laws. Moreover, the emergence of a companion noise non-detectable by devices is compliant with the law of conservation of energy.

Recently, internet providers have turned their attention toward, telecommunication constellations of satellites. These complex systems implie new challenges concerning the management of telecommunication ressources. In this context, the goal is to provide a maximum internet throughput to the constellation but also having a reliable service. This challenge was modeled with a NP-hard optimization problem known as the dynamic unsplittable flow with path-change penalties, we present and analyze several resolution methods and discuss their practical application to a constellation context.

Global Navigation Satellite System Reflectometry (GNSS-R) is a powerful way to retrieve information from a reflecting surface by exploiting GNSS as signals of opportunity. In dual antenna conventional GNSS-R architectures, the reflected signal is correlated with a clean replica to obtain the specular reflection point delay and Doppler estimates, which are further processed to obtain the GNSS-R product of interest. An important problem that may appear for low elevation satellites is signal crosstalk, that is the direct line-of-sight signal leaks into the antenna dedicated to the reflected signal. Such crosstalk may degrade the overall system performance if both signals are very close in time, similar to multipath in standard GNSS receivers, the reason why mitigation strategies must be accounted for. In this article: (i) we first provide a geometrical analysis to justify that the estimation performance is only affected for low height receivers; (ii) then, we analyze the impact of crosstalk if not taken into account, by comparing the single source conditional maximum likelihood estimator (CMLE) performance in a dual source context with the corresponding Cramér–Rao bound (CRB); (iii) we discuss dual source estimators as a possible mitigation strategy; and (iv) we investigate the performance of the so-called variance estimator, which is designed to eliminate the coherent signal part, compared to both the CRB and non-coherent dual source estimators. Simulation results are provided for representative GNSS signals to support the discussion. From this analysis, it is found that: (i) for low enough reflected-to-direct signal amplitude ratios (RDR), the crosstalk has no impact on standard single source CMLEs; (ii) for high enough signal-to-noise ratios (SNR), the dual source estimators are efficient irrespective of the RDR, then being a promising solution for any reflected signal scenario; (iii) non-coherent dual source estimators are also efficient at high SNR; and (iv) the variance estimator is efficient as long as the non-coherent part of the signal is dominant.

This paper studies the detection of anomalous crop development at the parcel-level based on an unsupervised outlier detection technique. The experimental validation is conducted on rapeseed and wheat parcels located in Beauce (France). The proposed methodology consists of four sequential steps: (1) preprocessing of synthetic aperture radar (SAR) and multispectral images acquired using Sentinel-1 and Sentinel-2 satellites, (2) extraction of SAR and multispectral pixel-level features, (3) computation of parcel-level features using zonal statistics and (4) outlier detection. The different types of anomalies that can affect the studied crops are analyzed and described. The different factors that can influence the outlier detection results are investigated with a particular attention devoted to the synergy between Sentinel-1 and Sentinel-2 data. Overall, the best performance is obtained when using jointly a selection of Sentinel-1 and Sentinel-2 features with the isolation forest algorithm. The selected features are co-polarized (VV) and cross-polarized (VH) backscattering coefficients for Sentinel-1 and five Vegetation Indexes for Sentinel-2 (among us, the Normalized Difference Vegetation Index and two variants of the Normalized Difference Water). When using these features with an outlier ratio of 10%, the percentage of detected true positives (i.e., crop anomalies) is equal to 94.1% for rapeseed parcels and 95.5% for wheat parcels.

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.

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.

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.

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?