Unambiguous sparse recovery of migrating targets with a robustified Bayesian model

Stéphanie Bidon; Marie Lasserre; François Le Chevalier

doi:10.1109/TAES.2018.2848360

Unambiguous sparse recovery of migrating targets with a robustified Bayesian model

Stéphanie Bidon, Marie Lasserre, François Le Chevalier

Microwave Sensing, Signals & Systems

Research output: Contribution to journal › Article › Scientific › peer-review

8 Citations (Scopus)

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Abstract

The problem considered is that of estimating unambiguously migrating targets observed with a wideband radar. We extend a previously described sparse Bayesian algorithm to the presence of diffuse clutter and off-grid targets. A hybrid-Gibbs sampler is formulated to jointly estimate the sparse target amplitude vector, the grid mismatch, and the (assumed) autoregressive noise. Results on synthetic and fully experimental data show that targets can be actually unambiguously estimated even if located in blind speeds.

Original language	English
Article number	8387428
Pages (from-to)	108-123
Number of pages	16
Journal	IEEE Transactions on Aerospace and Electronic Systems
Volume	55
Issue number	1
DOIs	https://doi.org/10.1109/TAES.2018.2848360
Publication status	Published - 2019

Bibliographical note

Accepted author manuscript

Keywords

Bayesian sparse recovery
high range resolution
Metropolis-adjusted Langevin algorithm
Monte Carlo Markov chain
range migration
velocity ambiguities
wideband radar

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10.1109/TAES.2018.2848360

Robustified Bayesian postprintAccepted author manuscript, 521 KB

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abstract = "The problem considered is that of estimating unambiguously migrating targets observed with a wideband radar. We extend a previously described sparse Bayesian algorithm to the presence of diffuse clutter and off-grid targets. A hybrid-Gibbs sampler is formulated to jointly estimate the sparse target amplitude vector, the grid mismatch, and the (assumed) autoregressive noise. Results on synthetic and fully experimental data show that targets can be actually unambiguously estimated even if located in blind speeds.",

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AB - The problem considered is that of estimating unambiguously migrating targets observed with a wideband radar. We extend a previously described sparse Bayesian algorithm to the presence of diffuse clutter and off-grid targets. A hybrid-Gibbs sampler is formulated to jointly estimate the sparse target amplitude vector, the grid mismatch, and the (assumed) autoregressive noise. Results on synthetic and fully experimental data show that targets can be actually unambiguously estimated even if located in blind speeds.

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KW - Metropolis-adjusted Langevin algorithm

KW - Monte Carlo Markov chain

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Unambiguous sparse recovery of migrating targets with a robustified Bayesian model

Abstract

Bibliographical note

Keywords

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