Algorithmes d’estimation et de détection en contexte hétérogène rang faible
Arnaud Breloy (Université Paris-Nanterre, FR)
November 06, 2016 — 10:30 — Location: None
Covariance Matrix (CM) estimation is an ubiquitous problem in statistical signal processing. In terms of application purposes, the accuracy of the CM estimate directly impacts the performance of the considered adaptive process. In the context of modern data-sets, two major problems are currently at stake: - Samples are often drawn from heterogeneous (non gaussian) distributions. - Only a low sample support is available. To respond to these problems, one has to develop new estimation tools that are based on an appropriate modeling of the data. Regarding to the first issue, the Complex Elliptically Symetric distributions framework have attracted lately lots of attention since it can account for the noise heterogeneity, thus lead to robusts estimators. As for the second the second issue, the true CM is often known to possess an inherent structure in many applications. This prior knowledge can be exploited to reduce the numbre of required samples in the estimation process. To enjoy best of both worlds, research currently focuses on ways to develop robust CM estimators with a constrained structure. In this talk, we will present a specific model, driven by radar applications (also more widely extendible), where the samples are drawn from a low rank low rank heterogeneous distribution (the so-called clutter) plus a white gaussian noise (thermal noise). We will present newly developed robust estimation methods of the CM parameters adapted to this context. The use of these new estimators will be illustrated in a Space time Adaptive Processing for airborne radar application.
Arnaud Breloy graduated from Ecole Centrale Marseille and recived a Master’s degree of Signal and Image Processing from university of Aix- Marseille in 2012-13. Formerly Ph.D student at the SATIE and SONDRA laboratories, he is currently lecturer at University Institute of Technology of Ville d’Avray. His research interests focuses on statistical signal processing, array and radar signal processing, robust estimation methods and low rank methods.