Wideband radar systems are highly resolved in range, which is a desirable feature for mitigating clutter. However, due to a smaller range resolution cell, moving targets are prone to migrate along the range during the coherent processing interval (CPI). This range walk, if ignored, can lead to huge performance degradation in detection. Even if compensated, conventional processing may lead to high sidelobes preventing from a proper detection in case of a multitarget scenario. Turning to a compressed sensing framework, we present a Bayesian algorithm that gives a sparse representation of migrating targets in case of a wideband waveform. Particularly, it is shown that the target signature is the sub-Nyquist version of a virtually well-sampled two-dimensional (2D)-cisoid. A sparse-promoting prior allows then this cisoid to be reconstructed and represented by a single peak without sidelobes. Performance of the proposed algorithm is finally assessed by numerical simulations on synthetic and semiexperimental data. Results obtained are very encouraging and show that a nonambiguous detection mode may be obtained with a single pulse repetition frequency (PRF).

In this paper, we address the problem of characterization of the GNSS pseudorange error in urban canyons. The goal of this work is to study the reliability of predicting the observation bias with a 3D GNSS simulation model. Comparison between simulated and real pseudorange bias allows the evaluation of the realism of the simulation model. We use the 3D multipath simulations into a mathematical reconstruction of the correlation function to estimate the bias at the output of the code tracking step, which provides an estimation of pseudorange error. The characteristics of the used 3D software, the collected real data and the comparison results are presented. We finish the paper by discussing different possibilities of integrating this kind of 3D city model inside the receiver processing. BIOGRAPHIES

In a MIMO radar network the multiple transmit elements may emit waveforms that differ on power and bandwidth. In this paper, we are asking, given that these two resources are limited, what is the optimal power, optimal bandwidth and optimal joint power and bandwidth allocation for best localization of multiple targets. The well known Cramer-Rao lower bound for target localization accuracy is used as a figure of merit and approximate solutions are found by minimizing a sequence of convex problems. Their quality is assessed through extensive numerical simulations and with the help of a lowerbound on the true solution. Simulations results reveal that bandwidth allocation policies have a definitely stronger impact on performance than power.

Non-analytic behavioral models of passive non linearity presented in 2013 are used to define and propose new specifications for multicarrier passive intermodulation in antennas. A margin of up to 10 dB is obtained in some cases and can be used to relax 2-carrier PIM specifications.

This paper introduces a redundancy adaptation algorithm based on an on-the-fly erasure network coding scheme named Tetrys in the context of real-time videotransmission. The algorithm exploits the relationship between the redundancy ratio used by Tetrys and the gain or loss in encoding bit rate from changing a video quality parameter called the Quantization Parameter (QP). Our evaluations show that with equal or less bandwidth occupation, the video protected by Tetrys with redundancy adaptation algorithm obtains a PSNR gain up to or more than 4 dB compared to the video without Tetrys protection. We demonstrate thatt he Tetrys redundancy adaptation algorithm performs well with the variations of both loss pattern and delay induced by the networks. We also show that Tetrys with the redundancy adaptation algorithm outperforms traditional block-based FEC codes with and without redundancy adaptation.

In this letter, we consider two sets of observations defined as subspace signals embedded in noise and we wish to analyze the distance between these two subspaces. The latter entails evaluating the angles between the subspaces, an issue reminiscent of the well-known Procrustes problem. A Bayesian approach is investigated where the subspaces of interest are considered as random with a joint prior distribution (namely a Bingham distribution), which allows the closeness of the two subspaces to be parameterized. Within this framework, the minimum mean-square distance estimator of both subspaces is formulated and implemented via a Gibbs sampler. A simpler scheme based on alternative maximum a posteriori estimation is also presented. The new schemes are shown to provide more accurate estimates of the angles between the subspaces, compared to singular value decomposition based independent estimation of the two subspaces.

Sparse signal/image recovery is a challenging topic that has captured a great interest during the last decades, especially in the biomedical field. Many techniques generally try to regularize the considered ill-posed inverse problem by defining appropriate priors for the target signal/image. The target regularization problem can then be solved either in a variational or Bayesian context. However, a little interest has been devoted to the uncertainties about the linear operator, which can drastically alter the reconstruction quality. In this paper, we propose a novel method for signal/image recovery that accounts and corrects the linear operator imprecisions. The proposed approach relies on a Bayesian formulation which is applied to EEG signal recovery. Our results show the promising potential of the proposed method compared to other regularization techniques which do not account for any error affecting the linear operator.

This letter considers the problem of computing the Cramer–Rao bound for the parameters of a Markov random field. Computation of the exact bound is not feasible for most fields of interest because their likelihoods are intractable and have intractable derivatives. We show here how it is possible to formulate the computation of the bound as a statistical inference problem that can be solve approximately, but with arbitrarily high accuracy, by using a Monte Carlo method. The proposed methodology is successfully applied on the Ising and the Potts models.

When considering the problem of unmixing hyperspectral images, most of the literature in the geoscience and image processing areas relies on the widely used linear mixing model (LMM). However, the LMM may be not valid, and other nonlinear models need to be considered, for instance, when there are multiscattering effects or intimate interactions. Consequently, over the last few years, several significant contributions have been proposed to overcome the limitations inherent in the LMM. In this article, we present an overview of recent advances in nonlinear unmixing modeling.

Cospas-Sarsat is an international search and rescue system that operates using low-orbit satellites and geostationary satellites. The current satellite constellation is being replaced by medium Earth orbit satellites which will cover larger areas of the surface of the Earth, permitting almost instantaneous alerts. The objective of this thesis is to study the localization performance of this new system, named MEOSAR (Medium Earth Orbit Search and Rescue). We first study the quality of the link between the beacon, the satellite and the ground receiving station through a link budget. Then, we propose a signal model based on sigmoidal functions to model the smooth transitions of the distress signal. For this model, the localization performance (in terms of Cramér-Rao bounds and estimator variances) is studied for the estimation of the beacon position and for different parameters including the time of arrival, the frequency of arrival and the symbol width. Then, we study the impact of adding prior information on the symbol width and the signal rise time, which are constructed from the allowed tolerances on the beacon specifications. We also investigate the error introduced by the addition of oscillator phase noise, and we show how the position estimation can be improved by taking into account multiple emissions of the beacon. Finally, the localization performance of the MEOSAR system is studied for second generation beacons, which are being developed using spread spectrum modulation.