New Sparse-Promoting Prior for the Estimation of a Radar Scene with Weak and Strong Targets

Lasserre, Marie; Bidon, Stéphanie; Chevalier, François Le

doi:10.1109/tsp.2016.2563409

Cited by 7 publications

(3 citation statements)

References 25 publications

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“…x , we use the results of [47] and accordingly choose a prior centered around high power values (with respect to the scatterers). Concretely, we set the mean m σ 2…”

Section: A Parameter Settingmentioning

confidence: 99%

Unambiguous Sparse Recovery of Migrating Targets With a Robustified Bayesian Model

Bidon

Lasserre

Chevalier

2019

IEEE Trans. Aerosp. Electron. Syst.

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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.

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“…x , we use the results of [47] and accordingly choose a prior centered around high power values (with respect to the scatterers). Concretely, we set the mean m σ 2…”

Section: A Parameter Settingmentioning

confidence: 99%

Unambiguous Sparse Recovery of Migrating Targets With a Robustified Bayesian Model

Bidon

Lasserre

Chevalier

2019

IEEE Trans. Aerosp. Electron. Syst.

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“…Sparse representation of two-dimensional (2-D) radar signatures has been widely used in many applications, such as super-resolution radar imaging, data compression, and target identification [1,2,3,4]. 2-D radar signatures can be reconstructed with fewer data, where a set of parameters including the locations, amplitudes, and damping factors are used to represent the returned signals from spatial distributed scattering centers.…”

Section: Introductionmentioning

confidence: 99%

Two-Dimensional Augmented State–Space Approach with Applications to Sparse Representation of Radar Signatures

Wu¹,

Xu²

2019

Sensors

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In this work, we focus on sparse representation of two-dimensional (2-D) radar signatures for man-made targets. Based on the damped exponential (DE) model, a 2-D augmented state–space approach (ASSA) is proposed to estimate the parameters of scattering centers on complex man-made targets, i.e., the complex amplitudes and the poles in down-range and aspect dimensions. An augmented state–space approach is developed for pole estimation of down-range dimension. Multiple-range search strategy, which applies one-dimensional (1-D) state–space approach (SSA) to the 1-D data for each down-range cell, is used to alleviate the pole-pairing problem occurring in previous algorithms. Effectiveness of the proposed approach is verified by the numerical and measured inverse synthetic aperture radar (ISAR) data.

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“…The main hypothesis making this theory applicable is that many satellites are not affected by MP making the biases sparse with respect to number of received measurements. Note that sparse estimation for mitigating multipath has already been considered in Radar theory [34], [35], and that sparse assumption was also considered for GNSS applications in [36]. However, the proposed approach is different.…”

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confidence: 99%

Multipath Mitigation for GNSS Positioning in an Urban Environment Using Sparse Estimation

Lesouple¹,

Robert

Sahmoudi

et al. 2019

IEEE Trans. Intell. Transport. Syst.

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Multipath (MP) remains the main source of error when using global navigation satellite systems (GNSS) in a constrained environment, leading to biased measurements and thus to inaccurate estimated positions. This paper formulates the GNSS navigation problem as the resolution of an overdetermined system whose unknowns are the receiver position and speed, clock bias and clock drift, and the potential biases affecting GNSS measurements. We assume that only a part of the satellites are affected by MP, i.e., that the unknown bias vector has several zero components, which allows sparse estimation theory to be exploited. The natural way of enforcing this sparsity is to introduce an 1 regularization associated with the bias vector. This leads to a least absolute shrinkage and selection operator problem that is solved using a reweighted-1 algorithm. The weighting matrix of this algorithm is designed carefully as functions of the satellite carrier-to-noise density ratio (C/N 0) and the satellite elevations. Experimental validation conducted with real GPS data show the effectiveness of the proposed method as long as the sparsity assumption is respected.

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New Sparse-Promoting Prior for the Estimation of a Radar Scene with Weak and Strong Targets

Cited by 7 publications

References 25 publications

Unambiguous Sparse Recovery of Migrating Targets With a Robustified Bayesian Model

Unambiguous Sparse Recovery of Migrating Targets With a Robustified Bayesian Model

Two-Dimensional Augmented State–Space Approach with Applications to Sparse Representation of Radar Signatures

Multipath Mitigation for GNSS Positioning in an Urban Environment Using Sparse Estimation

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