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.

Satellite communications can provide fourth generation (4G) networks with large-scale coverage. However, their integration to 4G is challenging because satellite networks have not been designed with handover in mind. The setup of satellite links takes time, and so, handovers must be anticipated long before. This paper proposes a generic scheme based on the Institute of Electrical and Electronics Engineers 802.21 standard to optimize handover and resource management in hybrid satellite-terrestrial networks. Our solution, namely optimized handover and resource management (OHRM), uses the terrestrial interface to prepare handover, which greatly speeds up the establishment of the satellite link. We propose two mechanisms to minimize the waste of bandwidth due to wrong handover predictions. First, we leverage the support of 802.21 in the terrestrial access network to shorten the path of the signaling messages towards the satellite resource manager. Second, we cancel the restoration of the satellite resources when the terrestrial link rolls back. We use OHRM to interconnect a digital video broadcasting and a wireless 4G terrestrial network. However for the simulation tool, we use a WiMAX as the terrestrial technology to illustrate the schemes. The simulation results show that OHRM minimizes the handover delay and the signaling overhead in the terrestrial and satellite networks.

The unprecedented success of wireless telecommunication systems has resulted in the wireless spectrum becoming a scarce resource. At the same time, extensive measurements conducted in the early 2000’s have shown that a significant part of the licensed spectrum, for instance that dedicated to TV broadcast services, is under-utilized. Cognitive Radio systems have been proposed as the enabling technology allowing unlicensed equipments, referred to as secondary users, to opportunistically access the licensed spectrum when not in use by the licensed users, referred to as primary users. Obviously, changing decades-old policies on spectrum access in the civilian and military domain will not happen overnight and many issues, technological and legal, will have to be ironed out first. However, the fundamental principle that we expect to underlie all solutions is that of access while doing no harm – secondary users should not interfere with primary users. The focus of this thesis is on heterogeneous tactical networks deploying cognitive radios in parts or in their entirety. Such networks can be organized in multiple sub-networks, each characterized by a specific topology, medium access scheme and spectrum access policy. As a result, providing end-to-end Quality of Service guarantees in terms of bandwidth, delay and jitter, emerges as a key challenge. We first address the admission control in multi-hop cognitive radio networks. We show that for this type of networks there is no algorithm capable of estimating the available end-to-end bandwidth in a distributed fashion. Therefore, we fill the gap by introducing a polynomial time algorithm that lands itself to a distributed implementation. Then, we focus on routing and propose a new metric that takes into account the specifics of such networks. Using empirical data from a USRP testbed we show that ETX, the de facto standard metric for wireless networks, fails in the cognitive radio context. Therefore, we revisit ETX by considering the effects of primary user activity and the implications of the principle of access while doing no harm. Finally, as quality of service requirements can be expressed using multiple metrics, we turn our attention to multi-constrained quality of service routing. With the underlying problem being NP-complete, we review the proposed heuristics and approximation algorithms. Our research reveals a trade-off between utilizing theoretically-proven but computably expensive algorithms and solutions that are fast but have poor worstcase bounds. To test the feasibility of solving multi-constrained routing in practice, we implement on a real testbed low complexity algorithms that extend the Dijkstra’s shortest path algorithm. We show that these algorithms can be incorporated in link state routing protocols, such as OSPF and OLSR. While these solutions suffer from poor worst-case performance bounds, in practice, they lead to satisfactory results when compared to exact but non tractable solutions.