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

Lasserre, Marie and Bidon, Stéphanie and Besson, Olivier and Le Chevalier, François Bayesian sparse Fourier representation of off-grid targets with application to experimental radar data. (2015) Signal Processing, 111. 261-273. ISSN 0165-1684

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Official URL: http://www.sciencedirect.com/science/article/pii/S0165168414005982

Abstract

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

Item Type:Article
Additional Information:Thanks to Elsevier editor. The definitive version is available at Science Direct: http://www.sciencedirect.com/science/journal/01651684/
HAL Id:hal-01111139
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:
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Deposited By: Marie Lasserre
Deposited On:29 Jan 2015 15:56

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