The objective of our paper is to improve efficiency (in terms of throughput or system capacity) for mobile satellite communications. In this context, we propose an enhanced Hybrid Automatic Repeat reQuest (HARQ) for delay tolerant services. Our proposal uses the estimation of the mutual information. We evaluate the performance of the proposed method for a land mobile satellite channel by means of simulations. Results are compared with those obtained with a classical incremental redundancy (IR) HARQ scheme. The technique we propose, shows a better performance in terms of efficiency while maintaining an acceptable delay for services.

Multifractal analysis is a powerful standard signal processing tool. Multifractal models are essentially characterized by two parameters, the so-called multifractality parameter c2 and the integral scale A (the time scale beyond which multifractal properties vanish). Yet, most applications concentrate on estimating c2 while estimating A is mostly overlooked, despite of A potentially conveying important information. Joint estimation of c2 and A is challenging due to the statistical nature of multifractal processes (strong dependence, non-Gaussian), and has barely been considered. The present contribution addresses these limitations and proposes a Bayesian procedure for the joint estimation of (c2;A). Its originality resides, first, in the construction of a generic multivariate model for the statistics of wavelet leaders for multifractal multiplicative cascade processes, and second, in the use of a suitable Whittle approximation for the likelihood associated with the model. The resulting model enables Bayesian estimators for (c2;A) to be computed also for large sample size. Performance is assessed numerically for synthetic multifractal processes and illustrated for wind-tunnel turbulence data. The proposed procedure significantly improves estimation of c2 and yields, for the first time, reliable estimates for A.

This paper proposes a new Bayesian strategy for the estimation of smooth parameters from nonlinear models. The observed signal is assumed to be corrupted by an independent and non identically (colored) Gaussian distribution. A prior enforcing a smooth temporal evolution of the model parameters is considered. The joint posterior distribution of the unknown parameter vector is then derived. A Gibbs sampler coupled with a Hamiltonian Monte Carlo algorithm is proposed which allows samples distributed according to the posterior of interest to be generated and to estimate the unknown model parameters/hyperparameters. Simulations conducted with synthetic and real satellite altimetric data show the potential of the proposed Bayesian model and the corresponding estimation algorithm for nonlinear regression with smooth estimated parameters.

This paper presents an unsupervised Bayesian algorithm for hyperspectral image unmixing accounting for endmember variability. The pixels are modeled by a linear combination of endmembers weighted by their corresponding abundances. However, the endmembers are assumed random to take into account their variability in the image. An additive noise is also considered in the proposed model generalizing the normal compositional model. The proposed algorithm exploits the whole image to benefit from both spectral and spatial information. It estimates both the mean and the covariance matrix of each endmember in the image. This allows the behavior of each material to be analyzed and its variability to be quantified in the scene. A spatial segmentation is also obtained based on the estimated abundances. In order to estimate the parameters associated with the proposed Bayesian model, we propose to use a Hamiltonian Monte Carlo algorithm. The performance of the resulting unmixing strategy is evaluated via simulations conducted on both synthetic and real data.

This paper addresses the problem of blind deconvolution for ultrasound images within a Bayesian framework. The prior of the unknown ultrasound image to be estimated is assumed to be a product of generalized Gaussian distributions. The point spread function of the system is also assumed to be unknown and is assigned a Gaussian prior distribution. These priors are combined with the likelihood function to build the joint posterior distribution of the image and PSF. However, it is difficult to derive closed-form expressions of the Bayesian estimators associated with this posterior. Thus, this paper proposes to build estimators of the unknown model parameters from samples generated according to the model posterior using a hybrid Gibbs sampler. Simulation results performed on synthetic data allow the performance of the proposed algorithm to be appreciated.

In this paper we propose the introduction of adaptive hybrid automatic repeat request (HARQ) in the context of mobile satellite communications. HARQ schemes which are commonly used in terrestrial links, can be adapted to improve the throughput for delay tolerant services. The proposed method uses the estimation of the mutual information between the received and the sent symbols, in order to estimate the number of bits necessary to decode the message at next transmission. We evaluate the performance of our method by simulating a land mobile satellite (LMS) channel. We compare our results with the static HARQ scheme, showing that our adaptive retransmission technique has better efficiency while keeping an acceptable delay for services.

It is well-known that the Extended Kalman Filer (EKF) is the standard estimation method for positioning with GNSS measurements. However, this filtering method is not optimal when the GNSS measurements become contaminated by non-Gaussian errors including multipath (MP) and non-line-of-sight (NLOS) errors. In this paper, we apply some techniques from robust statistic to make the conventional EKF more resistant to outliers which may be summed up as MP and NLOS signals in urban environments. We study two robust estimators that do not require tuning parameters fixed in advance: the first estimator detect the outliers using a robust statistical test based on the measure of the distance between each innovation sample with respect to the median of all innovations, then assign to them a low weight in the state estimation while keeping nominal weights for good Pseudo-Ranges (PR). The second estimator exploits the difference between two successive innovations to detect jumps related to large errors as MP and NLOS bias and correct their effect via a new recursive weighting technique. Test results using real GPS signal in downtown of Toulouse show that these estimators are simple to implement and capable of detecting multiple outliers in real-time and then improving the positioning accuracy compared to the conventional EKF.

Remote sensing images are commonly used to monitor the Earth surface evolution. This surveillance can be conducted by detecting changes between images acquired at different times and possibly by different kinds of sensors. A representative case is when an optical image of a given area is available and a new image is acquired in an emergency situation (resulting from a natural disaster for instance) by a radar satellite. In such a case, images with heterogeneous properties have to be compared for change detection. This paper proposes a new approach for similarity measurement between images acquired by heterogeneous sensors. The approach exploits the considered sensor physical properties and specially the associated measurement noise models and local joint distributions. These properties are inferred through manifold learning. The resulting similarity measure has been successfully applied to detect changes between many kinds of images, including pairs of optical images and pairs of optical-radar images.

Les systèmes de télécommunications par satellite en orbite géostationnaire ont récemment connu un regain d'intérêt, du fait de la très large couverture offerte, ainsi que de leur capacité importante. Cependant, la gestion des ressources fréquentielles dans ces systèmes est particulièrement complexe, du fait de deux phénomènes décorrélés : d'une par la dépendance de la qualité du lien aux conditions météorologiques, d'autre part la diversité des applications utilisées dans le système, et le caractère très dynamique de la demande des utilisateurs. Nous présentons ici une méthode optimale d'allocation de ressources, explicitant les compromis faits entre demande et conditions météorologiques.

Les codes LDPC ont été vite préférés aux turbo codes dans de nombreux standards (DVB-S2, DVB-T2, WI-MAX, Wimedia 1.5 UWB, G.hn, …) pour leurs très bonnes performances. Les codes LDPC basés sur des protographes bénéficient de plusieurs avantages d'un point de vue design et implémentation. L'évaluation des énumérateurs de poids (IOWE) est importante pour prédire les performances dans la region “error floor”. En revanche, leur calcul devient vite très complexe avec les grandes longueurs de code. Pour ce, Shadi Abu Surra a stipulé une conjecture qui permet de simplifier cette énumération. Dans cette présentation, on présentera les différentes méthodes proposées dans la littérature pour le calcul des IOWE, on donnera une preuve de la conjecture d'Abu Surra et on proposera une nouvelle méthode qui permet de diminuer considérablement la complexité de calcul avec une dégradation des résultats arbitrairement petite.