L'objectif général de la présentation est de déterminer les performances des futurs signaux 5G en terme de positionnement. Pour ce faire, l'étude se focalise sur la définition d'un modèle mathématique de sorties de corrélateurs 5G. D'après les standards 3GPP, la 5G utilisera des signaux OFDM (Orthogonal Frequency Division Multiplexing). Dans la littérature, des modèles mathématiques aux différentes étapes du traitement (modulation, démodulation, corrélation, boucle de poursuite,...) pour signaux OFDM sont déjà disponible. Ces modèles sont développés en supposant un canal de propagation constant pendant la durée d'un symbole OFDM. En vue d'adapter ces résultats, ces modèles, à la 5G, il a été nécessaire de sélectionner un canal de propagation répondant aux critères des standards 3GPP pour la 5G. Le choix s'est porté sur le simulateur QuaDRiGa ; une étude approfondie des modèles générés par ce simulateur a montré que le canal de propagation ne pouvait être considéré comme constant sur la durée d'un symbole OFDM. Ainsi, les modèles développés dans la littérature ne sont pas utilisables tels quels. Dans cette présentation, de nouveaux modèles mathématiques, prenant en compte cette évolution de canal, sont développés et appliqués pour le calcul de pseudo- distance 5G.

Single-photon lidar has emerged as a prime candidate technology for depth imaging through challenging environments. Until now, a major limitation has been the significant amount of time required for the analysis of the recorded data. Here we show a new computational framework for real-time three-dimensional (3D) scene reconstruction from single-photon data. By combining statistical models with highly scalable computational tools from the computer graphics community, we demonstrate 3D reconstruction of complex outdoor scenes with processing times of the order of 20ms, where the lidar data was acquired in broad daylight from distances up to 320metres. The proposed method can handle an unknown number of surfaces in each pixel, allowing for target detection and imaging through cluttered scenes. This enables robust, real-time target reconstruction of complex moving scenes, paving the way for single-photon lidar at video rates for practical 3D imaging applications.

Joint deconvolution and segmentation of ultrasound images is a challenging problem in medical imaging. By adopting a hierarchical Bayesian model, we propose an accelerated Markov chain Monte Carlo scheme where the tissue reflectivity function is sampled thanks to a recently introduced proximal unadjusted Langevin algorithm. This new approach is combined with a forward-backward step and a preconditioning strategy to accelerate the convergence, and with a method based on the majorizationminimization principle to solve the inner nonconvex minimization problems. As demonstrated in numerical experiments conducted on both simulated and in vivo ultrasound images, the proposed method provides high-quality restoration and segmentation results and is up to six times faster than an existing Hamiltonian Monte Carlo method.

The purpose of this paper is to introduce a customized multi- modality phantom designed to facilitate the proof-of-concept of MRI/ultrasound fusion approaches. Phantom experiments are often required before in vivo validation, giving access to more challenging data than numerical simulations. Nevertheless, manufactured phantoms are expensive and usually lack of flexibility. In contrast, the proposed model was inexpensive and accurately designed to overcome multimodal registration issues.

Estimating the cardiac motion from ultrasound (US) images is an ill-posed problem that requires regularization. In a recent study, it was shown that constraining the cardiac motion fields to be patch-wise sparse in a learnt overcomplete motion dictionary is more accurate than local parametric models (affine) or global functions (B-splines, total variation). In this work, we extend this method by incorporating temporal smoothness in a multi-frame optical-flow (OF) strategy. An efficient optimization strategy using the constrained split augmented Lagrangian shrinkage algorithm (C-SALSA) is proposed. The performance is evaluated on a realistic simulated cardiac dataset with available ground-truth. A comparison with the pairwise approach shows the interest of the proposed temporal regularization and multi-frame strategy in terms of accuracy and computational time.

n multipath (MP) environments, the received signals depend on several factors related to the global navigation satellite systems (GNSS) receiver environment and motion. Thus it is difficult to use a specific propagation model to accurately capture the dynamics of the MP signal when the GNSS receiver is moving in urban canyons. This paper formulates the problem of MP interference mitigation in the GNSS receiver as a joint state (containing the direct signal parameters) and time-varying model parameter (containing the MP signal parameters) estimation. Accordingly, we propose to exploit the EM algorithm for achieving the joint state and time-varying parameter estimation in the context of MP interference mitigation in GNSS receivers. More precisely, the proposed EM-based MP mitigation approach is decomposed into two iterative steps: (a) the posterior pdf of the direct signal parameters and the expected log-likelihood function necessary in the expectation step of the EM algorithm are approximated by using an appropriate particle filter; (b) the maximum likelihood solution for MP signal parameters is then obtained using Newton’s method in the maximization step. The convergence of the proposed approach is analyzed based on the existing convergence theorem associated with the EM algorithm. Finally, a comprehensive simulation study is conducted to compare the performance of the proposed EM-based MP mitigation approach with other state-of-the-art MP mitigation approaches in static and realistic scenarios.

The objective of this paper is to determine the ranging performance of the upcoming fifth generation (5G) signal. In order to do so, it is required to define 5G correlator outputs mathematical models. 5G systems will use OFDM (Orthogonal Frequency Division Multiplexing) signals; in the literature, mathematical models of OFDM signals are developed at the different receiver signal processing stages. These models assumed that the propagation channel is constant over an OFDM symbol; nevertheless, an in-depth study of QuaDRiGa, a 5G compliant propagation channel simulator, invalidates this hypothesis. Therefore, in this paper, mathematical models are developed that take into account the channel evolution. The focus is given on correlator outputs and results are applied to the computation of 5G based pseudo range accuracy.

Error correcting schemes are fundamental in the new generation of data navigation signals. Thanks to those, the system has the capability to correct possible data navigation errors, which potentially induces delays in first fix of the receiver. In the GNSS receivers, those error correcting schemes use the Log Likelihood Ratio (LLR) as the input of the decoding algorithm. Until now, the LLR was always computed under the Gaussian assumption and considering perfect Complete State Information (CSI), which does not hold in most of the real scenarios. Then, in this paper we proposed several methods to compute the LLR, considering a set of realitic scenarios and considering that perfect CSI is not available at the receiver. We test the proposed LLRs for several new generation GNSS signals.

New generation of GNSS systems seeks to provide new features in order to create or to improve their currents services. Between those possible features; the increase of the data rate is necessary in order to to provide services such as authentication, precise positioning or reduce the Time-To-First-Fix (TTFF). On the other hand, the data availability in harsh environment suggest the need of error correcting technologies. Then, based on previous works over the Code-Shift Keying (CSK) modulation and in Root Protograph LDPC code to reduce the TTFF, in this paper, it is presented the optimization of Root Protograph LDPC codes for the CSK modulation in a Bit-Interleaved Coded Modulation context and the optimization of Root Protograph LDPC codes for the CSK modulation in Bit-Interleaved Coded Modulation Iterative Decoding context. Both optimization where base on the Protograph EXIT chat algorithm, providing promising results.

In this article, we provide the method to construct two error correcting structures for GNSS systems, which are capable to provide Maximum Distance Separable (MDS), full diversity and rate-compatible properties. Thanks to those properties, the GNSS receiver is capable to reduce the Time-To-First-Fix (TTFF) and to enhance the robustness of the data demodulation under low Carrier to Noise ratio environments, urban environments and pulsed jamming environments. The proposed error correcting structures are then simulated and compared with the GPS L1C subframe 2 error correcting scheme under the precedent transmission environments. Simulations show an outstanding improvement of the error correction capabilities (which reduce the TTFF in harsh environments) mainly caused by the rate-compatible and the full diversity properties. Moreover, thanks to the MDS property a high reduction of the TTFF under good environments is appreciated.