SB
S Bidon
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4 records found
1
Journal article
(2019)
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Stéphanie Bidon, Marie Lasserre, François Le Chevalier
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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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.
Pulse-Doppler radars classically transmit a train of pulses with a narrow bandwidth at a constant pulse repetition frequency (PRF) to detect moving targets. By design, they are subject to the well-known problem of range and/or velocity ambiguities. Indeed, the ambiguous velocity v a and range ambiguity R a are linked through
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Pulse-Doppler radars classically transmit a train of pulses with a narrow bandwidth at a constant pulse repetition frequency (PRF) to detect moving targets. By design, they are subject to the well-known problem of range and/or velocity ambiguities. Indeed, the ambiguous velocity v a and range ambiguity R a are linked through
Conference paper
(2016)
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M Lasserre, Stéphanie Bidon, Francois le Chevalier
Within the scope of sparse signal representation, we consider the problem of velocity ambiguity mitigation for wideband radar signal. We present a Bayesian robust algorithm based on a new sparsifying dictionary suited for range-migrating targets possibly straddling range-velocity bins. Numerical simulations
on experimental data demonstrate the ability of the proposed algorithm in mitigating velocity ambiguity. ...
on experimental data demonstrate the ability of the proposed algorithm in mitigating velocity ambiguity. ...
Within the scope of sparse signal representation, we consider the problem of velocity ambiguity mitigation for wideband radar signal. We present a Bayesian robust algorithm based on a new sparsifying dictionary suited for range-migrating targets possibly straddling range-velocity bins. Numerical simulations
on experimental data demonstrate the ability of the proposed algorithm in mitigating velocity ambiguity.
on experimental data demonstrate the ability of the proposed algorithm in mitigating velocity ambiguity.
Journal article
(2016)
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Marie Lasserre, Stéphanie Bidon, François Le Chevalier
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.
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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.