Bayesian Sparse Estimation of a Radar Scene with Weak and Strong Targets

Marie Lasserre (1) , Stéphanie Bidon (1) , Olivier Besson (1) , François Le Chevalier (2)

1 DEOS - Département Electronique, Optronique et Signal
2 Department of Electrical Engineering, Mathematics and Computer Science [Delft]

We consider the problem of estimating a finite number of atoms of a dictionary embedded in white noise, using a sparse signal representation (SSR) approach, a problem which is relevant in many radar applications. In particular, the estimation of a radar scene consisting of targets with wide amplitude range can be challenging since the sidelobes of a strong target can disrupt the estimation of a weak one. In this paper, we present a Bayesian algorithm able to estimate weak targets possibly hidden by strong ones. The main strength of this algorithm lies in a novel sparse-promoting prior distribution which decorrelates sparsity level and target power and makes the estimation process span the whole target power range. This algorithm is implemented through a Monte-Carlo Markov chain. It is successfully evaluated on synthetic and semiexperimental radar data.

Mots clés: Sparse representation; Bayesian estimation; Monte Carlo Markov Chain

Domaines: Autre

Marie Lasserre, Stéphanie Bidon, Olivier Besson, François Le Chevalier. Bayesian Sparse Estimation of a Radar Scene with Weak and Strong Targets. 2015 3rd International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar, and Remote Sensing (CoSeRa), Jun 2015, Pisa, Italy. pp. 51-55. ⟨hal-01446139⟩