Considered as the free accessible and suitable solution for positioning in urban areas, Global Navigation Satellite Systems (GNSS) have been widely used these recent years in a wide spectrum of applications. However, signal blockage, non-line-of-sight (NLOS) multipath interferences and signal degradation affect the system performance and represent the major hurdles of GNSS in it course of adoption as a main localization technology in urban environments. Many approaches have been employed to constructively use these degraded signals in order to reduce positioning errors. Following this vision, we propose in this paper a joint estimation method of the position and the bias for measurement correction. This formulation leads to an ill-conditioned estimation problem. In this work, we apply a regularized robust estimation framework to this problem of NLOS mitigation for GNSS positioning in harsh areas. We derive the optimal regularization matrix by minimizing the total Mean Square Errors (MSE) of the considered model. The performance of the proposed method is assessed using real GNSS data collected in a dense urban area in Toulouse City, showing improvements in comparison to some existing methods.

Le procédé de positionnement comprend l'estimation d'un ensemble de biais multitrajets sur la base d'un modèle théorique des écarts entre les pseudo-distances mesurées et les pseudo-distances estimées. Le modèle comprend un terme de géométrie dépendant de la position du récepteur GNSS et des émetteurs GNSS et un terme additif représentant l'ensemble desdits biais. Les inconnues du modèle comprennent la position courante du récepteur GNSS et lesdits biais. L'estimation des biais repose sur une minimisation des résidus entre les écarts et le modèle théorique, en fonction des inconnues, sous la contrainte que l'ensemble des biais doit être parcimonieux. Les pseudo-distances mesurées sont corrigées sur base des biais estimés et une position courante du récepteur GNSS est prédite sur la base des pseudo-distances mesurées ainsi corrigées. L'invention permet de réduire l'impact des multi-trajets sur la solution de positionnement et donc d'en améliorer l'exactitude.

This study deals with the sense and avoid problem for an helicopter. The objective of such a system is to early detect collision targets (typically high-voltage wires). The direction of arrival (DoA) of the target is then a crucial information. In severe multipath environments (flight over a river, for instance), classical DoA estimation schemes dramatically degrade. The authors make use of a method based on the maximum likelihood (ML) principle that can resolve two highly correlated and close targets. The major drawback of ML algorithms, namely the computational burden, is removed using an approximation for closely space targets. The contribution of this study is twofold. The authors first extend the approximated ML DoA estimation to the case of non-uniform linear antennas and complete the procedure by a detection scheme. Second, they attest the validity of this new processing on real radar data. Hence, they show that the proposed procedure is able to detect a high-voltage wire, over a river, at ranges up to 1 km, where a capon beamformer cannot.

A reliable leadfield matrix is needed to solve the magnetoencephalography/electroencephalography (M/EEG) source localization problem. The computation of this matrix requires several physical parameters, including the conductivity of the tissues that compose the subject's head. Since it is not precisely known, we modify a recent Bayesian algorithm to estimate the skull conductivity jointly with the brain activity directly from the M/EEG measurements. Synthetic and real data are used to compare our technique with two optimization algorithms, showing that the proposed method is able to provide results of similar or better quality with the advantage of being applicable in a more general case.

Pour faire face à l’augmentation des transferts de contenu via internet, les CDN (Content Delivery Network) ont été créés. Ces réseaux de caches permettent à la fois de réduire la charge chez les fournisseurs de contenus ainsi que les transitaires et d’améliorer l’expérience des utilisateurs. Cependant, certaines personnes restent tributaires de réseaux terrestres bas débit. L’utilisation de réseaux hybrides satellite-terrestre permettent de fournir à ces utilisateurs une meilleur expérience que chacun des réseaux indépendamment, mais les solutions actuelles n’en tirent pas pleinement profit. Nous verrons d’une part quelles sont les problématiques spécifiques aux CDNs, et d’autre part comment ceux-ci peuvent s’intégrer dans une architecture hybride et les bénéfices qu’ils peuvent y apporter.

Les modulations à phase continue (CPM) se distinguent des autres modulations de par leur enveloppe constante et une phase continue. Ses deux dernières caractéristiques mettent en lumière les avantages de l'utilisation de la CPM. Tout d'abord une enveloppe constante entraîne une puissance constante de l'onde porteuse et donc à un bon rendement en puissance. Cela peut être très utile lors de l'amplification du signal notamment par les relais satellites. Ensuite la continuité de la phase permet d'obtenir une forte efficacité spectrale. En conséquence les CPM sont utilisées dans de nombreux standards tels que le GSM, l'AIS, RCS2... Nous verrons lors de ce séminaire une introduction aux CPM et à leurs récepteurs associés ainsi que les contributions apportées par nos deux thèses.

We consider deterministic parameter estimation and the situation where the probability density function (p.d.f.) parameterized by unknown deterministic parameters results from the marginalization of a joint p.d.f. depending on random variables as well. Unfortunately, in the general case, this marginalization is mathematically intractable, which prevents from using the known standard deterministic lower bounds (LBs) on the mean squared error (MSE). Actually the general case can be tackled by embedding the initial observation space in a hybrid one where any standard LB can be transformed into a modified one fitted to nonstandard deterministic estimation, at the expense of tightness however. Furthermore, these modified LBs (MLBs) appears to include the submatrix of hybrid LBs which is an LB for the deterministic parameters.Moreover, since in the nonstandard estimation, maximum likelihood estimators (MLEs) can be no longer derived, suboptimal nonstandard MLEs (NSMLEs) are proposed as being a substitute. We show that any standard LB on the MSE of MLEs has a nonstandard version lower bounding the MSE of NSMLEs. We provide an analysis of the relative performance of the NSMLEs, as well as a comparison with the MLBs for a large class of estimation problems. Last, the general approach introduced is exemplified, among other things, with a new look at the well-known Gaussian complex observation models.

Multisymbol receiver is an effective method to demodulate noncoherent sequences. However it is necessary to correlate an important number of symbols in a noncoherent scheme to reach the performances carried out by optimal coherent Maximum a Posteriori (MAP) detectors such as BCJR. In this paper, we propose an advanced multisymbol receiver by adding some memory to the decision process. The advanced receiver, called here Multisymbol With Memory (MWM) takes into account the cumulative phase information unlike multisymbol algorithm and thus it can be seen as a truncated BCJR. An exact mathematical derivation is performed for this truncated BCJR. Then an implementation of the MWM detector applied to a continuous phase frequency shift keying modulation is presented. Finally an asymptotic analysis is carried out based on the achievable Symmetric Mutual Information rate. The proposed system exhibits good performances compared to classical multisymbol receivers at the expense of increased complexity and can approach the performances of a coherent receiver.

In this paper, we present a new data-aided carrier-recovery method for Continuous Phase Modulation (CPM) signals over frequency-selective channels. We first present a linear model of the received signal based on Mengali representation over selective channels and show how to use it to perform joint channel and carrier-frequency estimation. We also derive a low-complexity version of the estimator. Simulation results show that this method performs better than the optimal method suited to the Additive White Gaussian Noise (AWGN) channels.