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New Sparse-Promoting Prior for the Estimation of a Radar Scene with Weak and Strong Targets

Lasserre, Marie and Bidon, Stéphanie and Le Chevalier, François New Sparse-Promoting Prior for the Estimation of a Radar Scene with Weak and Strong Targets. (2016) IEEE Transactions on Signal Processing, 64 (7). 4634-4643. ISSN 1053-587X

(Document in English)

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Official URL: http://dx.doi.org/10.1109/TSP.2016.2563409


In this paper, we consider the problem of estimating a signal of interest embedded in noise using a sparse signal representation (SSR) approach. This problem is relevant in many radar applications. In particular, estimating 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. Within a Bayesian framework, we present a new sparse-promoting prior designed to estimate this specific type of radar scene. The main strength of this new prior lies in its mixed-type structure which decorrelates sparsity level and target power, as well as in its subdivided support which enables the estimation process to 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 and compared to state-of-the-art algorithms.

Item Type:Article
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution:Université de Toulouse > Institut Supérieur de l'Aéronautique et de l'Espace - ISAE-SUPAERO (FRANCE)
Other partners > Delft University of Technology - TU Delft (NETHERLANDS)
Laboratory name:
Deposited On:31 Aug 2016 11:47

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