Robust PCA for Through-the-Wall Radar Imaging

Hugo, Brehier,; Breloy, Arnaud; Ren, Chengfang; Hinostroza, Israel; Ginolhac, Guillaume

doi:10.23919/eusipco55093.2022.9909960

Cited by 3 publications

(10 citation statements)

References 48 publications

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“…We first describe a generic model for 2D TWRI [7,8]. A radar makes several measures sequentially over an axis parallel to the wall to penetrate with targets supposed to be few and small w.r.t.…”

Section: Signal Modelmentioning

confidence: 99%

“…In this paper, we study the detection of stationary targets in a way that is robust to heterogeneous wall clutter. This topic of robustness has been studied for Ground Penetrating Radar [3,4] and TWRI [5] which added onto nonrobust methods [6,7]. In a setting of measures obtained parallel to the wall, front wall interferences form a low rank matrix.…”

Section: Introductionmentioning

confidence: 99%

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Through-The-Wall Radar Imaging With Wall Clutter Removal Via Riemannian Optimization On The Fixed-Rank Manifold

Brehier,

Breloy,

Ren

et al. 2024

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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We introduce a new method for Through-the-Wall Radar Imaging (TWRI) that detects the location of stationary targets hidden by a wall. A crucial step is the mitigation of wall returns which obscure the scene and which are characterized by their low-rankedness given the radar measurement setup. Whereas existing methods make use of nuclear norm minimization or Truncated Singular Value Decomposition (TSVD), we propose to leverage Riemannian optimization over the manifold of fixed-rank matrices in order to use robust estimation while keeping the original rank constraint without relaxation. A detection step via sparse recovery is then performed and the overall method is compared with existing methods over simulated scenes. The results show that the proposed method achieves a better performance.

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Section: Signal Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Through-The-Wall Radar Imaging With Wall Clutter Removal Via Riemannian Optimization On The Fixed-Rank Manifold

Brehier,

Breloy,

Ren

et al. 2024

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…Within (22), S s,a (x) denotes the sparsity soft thresholding (ST) operator, which in this work is defined for complex numbers as…”

Section: Convex Relaxations Algorithmsmentioning

confidence: 99%

“…For a fixed γ, the optimization Problem ( 42) can be solved by use of the iterative thresholding (IT) approach, similar to ISTA (22), by iteratively conducting…”

Section: Complex Successive Concave Sparsity Approximationmentioning

confidence: 99%

“…The compressive sensing (CS), affine rank minimization (ARM), and compressed robust principal component analysis (CRPCA) methods are enjoying great popularity in terms of their application in modern radar signal processing. At present, they are being applied in almost every possible field of application ranging from basic radar signal processing [1][2][3][4] to more sophisticated applications such as multiple-input multiple-output (MIMO) radar [5,6], ground moving target indication (GMTI) [7][8][9][10][11], synthetic aperture radar (SAR) [12][13][14], inverse synthetic aperture radar (ISAR) [15][16][17][18], interference and clutter mitigation [19][20][21][22], or SAR-GMTI [23][24][25], imaging [26], and passive radar [27], to name a few-this list is far from being exhaustive. Many more examples of CS applied to radar signal processing can be found e.g., in [28].…”

Section: Introductionmentioning

confidence: 99%

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Fast, Efficient, and Viable Compressed Sensing, Low-Rank, and Robust Principle Component Analysis Algorithms for Radar Signal Processing

Panhuber

2023

Remote Sensing

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Modern radar signal processing techniques make strong use of compressed sensing, affine rank minimization, and robust principle component analysis. The corresponding reconstruction algorithms should fulfill the following desired properties: complex valued, viable in the sense of not requiring parameters that are unknown in practice, fast convergence, low computational complexity, and high reconstruction performance. Although a plethora of reconstruction algorithms are available in the literature, these generally do not meet all of the aforementioned desired properties together. In this paper, a set of algorithms fulfilling these conditions is presented. The desired requirements are met by a combination of turbo-message-passing algorithms and smoothed ℓ0-refinements. Their performance is evaluated by use of extensive numerical simulations and compared with popular conventional algorithms.

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Wall Clutter Mitigation Techniques Comparaison for 3D Through-the-Wall Imaging

Boudrouz,

Serhir,

Hinostroza

et al. 2024

2024 IEEE International Symposium on Antennas and Propagation and INC/USNC‐URSI Radio Science Meeting (AP-S/INC-USNC-URSI)

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Robust PCA for Through-the-Wall Radar Imaging

Abstract: detection via RPCA • Addition of a structured robust distance for heterogeneous noises • Resolution via ADMM coupled with variable splitting • Analysis of the proposed the method on FDTD simulations

Cited by 3 publications

References 48 publications

Through-The-Wall Radar Imaging With Wall Clutter Removal Via Riemannian Optimization On The Fixed-Rank Manifold

Through-The-Wall Radar Imaging With Wall Clutter Removal Via Riemannian Optimization On The Fixed-Rank Manifold

Fast, Efficient, and Viable Compressed Sensing, Low-Rank, and Robust Principle Component Analysis Algorithms for Radar Signal Processing

Wall Clutter Mitigation Techniques Comparaison for 3D Through-the-Wall Imaging

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