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

Abstract : 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.
Document type :
Conference papers
Complete list of metadatas

Cited literature [11 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-01446139
Contributor : Open Archive Toulouse Archive Ouverte (oatao) <>
Submitted on : Wednesday, January 25, 2017 - 4:15:56 PM
Last modification on : Thursday, May 31, 2018 - 4:58:02 PM
Long-term archiving on: Wednesday, April 26, 2017 - 3:39:03 PM

File

Lasserre_16750.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01446139, version 1
  • OATAO : 16750

Citation

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⟩

Share

Metrics

Record views

40

Files downloads

145