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

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

Lasserre, Marie (Université de Toulouse)
Bidon, Stéphanie (Université de Toulouse)
le Chevalier, F. (TU Delft Microwave Sensing, Signals & Systems)

2016

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.

sparse representation
Bayesian estimation
Monte Carlo Markov Chain

http://resolver.tudelft.nl/uuid:92e5e5a8-0c37-4868-a339-8d5e957e3bdc

https://doi.org/10.1109/TSP.2016.2563409

2018-09-30

1053-587X

IEEE Transactions on Signal Processing, 64 (17), 4634-4643

Accepted author manuscript

Institutional Repository

journal article

© 2016 Marie Lasserre, Stéphanie Bidon, F. le Chevalier