Bayesian sparse estimation of migrating targets in autoregressive noise for wideband radar

Stéphanie Bidon (1) , Olivier Besson (1) , Jean-Yves Tourneret (2, 3, 4) , François Le Chevalier (5)

1 DEOS - Département Electronique, Optronique et Signal
2 IRIT-MINDS - CoMputational imagINg anD viSion
3 TéSA - Télécommunications Spatiales et Aéronautiques - Telecommunications for Space ant Aeronautics
4 Toulouse INP - Institut National Polytechnique (Toulouse)
5 TU Delft - Delft University of Technology

In recent work we showed the interest of using sparse representation techniques to estimate a target scene observed by wideband radar systems. However the principle was demonstrated in a white noise background only. In this paper, we present an extended version of our sparse estimation technique that attempts to take into account the (possible) presence of diffuse clutter. More specifically, an autoregressive model is considered for the noise vector. Performance of the technique is studied on synthetic and experimental data. Pertinence of the noise model is discussed.

Mots clés: Radar; Bayes methods; Clutter; Vectors; Noise; Wideband; Estimation

Domaines: Synthèse d'image et réalité virtuelle [cs.GR]; Traitement du signal et de l'image [eess.SP]; Traitement des images [eess.IV]; Vision par ordinateur et reconnaissance de formes [cs.CV]; Intelligence artificielle [cs.AI]

Stéphanie Bidon, Olivier Besson, Jean-Yves Tourneret, François Le Chevalier. Bayesian sparse estimation of migrating targets in autoregressive noise for wideband radar. IEEE International Radar Conference (RadarCon 2014), May 2014, Cincinnati, OH, United States. pp. 579-584. ⟨hal-01493782⟩