Bayesian sparse Fourier representation of off-grid targets with application to experimental radar data

The problem considered is the estimation of a finite number of cisoids embedded in white noise, using a sparse signal representation (SSR) approach, a problem which is relevant in many radar applications. Many SSR algorithms have been developed in order to solve this problem, but they usually are se...

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Bibliographic Details
Published in:Signal processing 2015-06, Vol.111, p.261-273
Main Authors: Lasserre, Marie, Bidon, Stéphanie, Besson, Olivier, Le Chevalier, François
Format: Article
Language:English
Subjects:
Bayesian inference
Computer Science
Grid mismatch
Monte-Carlo Markov Chain
Signal and Image Processing
Sparse representation
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Summary:The problem considered is the estimation of a finite number of cisoids embedded in white noise, using a sparse signal representation (SSR) approach, a problem which is relevant in many radar applications. Many SSR algorithms have been developed in order to solve this problem, but they usually are sensitive to grid mismatch. In this paper, two Bayesian algorithms are presented, which are robust towards grid mismatch: a first method uses a Fourier dictionary directly parametrized by the grid mismatch while the second one employs a first-order Taylor approximation to relate linearly the grid mismatch and the sparse vector. The main strength of these algorithms lies in the use of a mixed-type distribution which decorrelates sparsity level and target power. Besides, both methods are implemented through a Monte-Carlo Markov chain algorithm. They are successfully evaluated on synthetic and experimental radar data, and compared to a benchmark algorithm.
ISSN:0165-1684
1872-7557
DOI:10.1016/j.sigpro.2014.12.024