Bayesian sparse estimation of migrating targets for wideband radar

Bidon, Stéphanie; Tourneret, Jean-Yves; Savy, Laurent; Chevalier, François Le

doi:10.1109/taes.2013.120533

Cited by 28 publications

(90 citation statements)

References 29 publications

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“…The signature of a moving target observed by a wideband radar is commonly expressed after applying fast Fourier transform (FFT) on fast time, thus in fast-frequency/slow-time domain, where it can be written as a bi-dimensional complex sinusoid with the coupling term modeling range migration [3], [12], [24]. With that said, the target signature in fast-frequency / slow-time is given by K × M matrix T ft defined element-wise:…”

Section: A Target Modelmentioning

confidence: 99%

“…The level of these ambiguous sidelobes is typically 6-20 dB, depending on the time-bandwidth product of transmitted pulse train [12], [25]. High resolution spectrum techniques applied to such data benefit from range migration effect resulting in ability to estimate the rangevelocity map in low PRF mode unambiguously [3], [12], [31]. For weak targets of interest, a simple compensation of the range-walk can be sufficient to remove velocity ambiguities.…”

Section: Introductionmentioning

confidence: 99%

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Detection of Range Migrating Targets in Compound-Gaussian Clutter

Petrov

Chevalier

Yarovoy

2018

IEEE Trans. Aerosp. Electron. Syst.

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Abstract-This paper deals with the problem of coherent radar detection of fast moving targets in a high range resolution mode. In particular, we are focusing on the spiky clutter modeled as a compound Gaussian process with rapidly varying power along range. Additionally, a fast moving target of interest has a few range cells migration within the coherent processing interval. Two coherent CFAR detectors are proposed taking into account target migration and highly inhomogeneous clutter. Both detectors involve solution of a transcendental equation, carried out numerically in a few iterations. The performance evaluation is addressed by numerical simulations and it shows a significant improvement in detection of fast moving targets in inhomogeneous heavy tailed radar clutter.

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Section: A Target Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detection of Range Migrating Targets in Compound-Gaussian Clutter

Petrov

Chevalier

Yarovoy

2018

IEEE Trans. Aerosp. Electron. Syst.

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“…In [6], a Bayesian sparse recovery algorithm was developed and proved to give good performance on synthetic and experimental data. As in every sparse signal representation (SSR) approach, the signal is described as a linear combination of a finite number of atoms from a dictionary.…”

Section: Introductionmentioning

confidence: 99%

“…However, as in every SSR algorithm, adequate tuning of some parameters is essential since it can deteriorate the performance of the reconstruction. In [6], the sparsity is enforced via a sparse-promoting prior distribution on vector x. This prior depends on some hyperparameters that will adjust the knowledge the radar operator has about the target power level.…”

Section: Introductionmentioning

confidence: 99%

“…In this case, setting up these hyperparameters can be tedious. This paper aims at modifying the algorithm proposed in [6] in order to deal with targets having significant different power level. The approach proposed is to modify the prior distribution of the sparse vector and to divide its support into several classes, which correspond to subdivisions of the receiver dynamic range.…”

Section: Introductionmentioning

confidence: 99%

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Bayesian sparse estimation of a radar scene with weak and strong targets

Lasserre

Bidon

Besson

et al. 2015

2015 3rd International Workshop on Compressed Sensing Theory and Its Applications to Radar, Sonar and Remote Sensing (CoSeRa)

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OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. 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.

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