Joint SAR Imaging and Multi-Feature Decomposition From 2-D Under-Sampled Data Via Low-Rankness Plus Sparsity Priors

Moradikia, Majid; Samadi, Sadegh; Çetin, Müjdat

doi:10.1109/tci.2018.2881530

Cited by 29 publications

(9 citation statements)

References 41 publications

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“…In this section, the effectiveness of the proposed approach is verified through both simulated data and real data of the Yak‐42 aircraft. For illustrating the performance of the proposed algorithm, five algorithms are used, which are the conventional RDA, the SL0 [8, 9 ], the Bayesian compressed sensing (BCS) of [14, 15 ], the ADMM [12 ], and the proposed FADMM method. All computations were run using MATLAB in Windows 7 on an Intel Core i5‐3210M CPU at 2.5 GHz.…”

Section: Resultsmentioning

confidence: 99%

“…Note that, although (14 ) has analytical solution, the computation of matrix inverse is impractical for large values of

I

. Therefore, a conjugate‐gradient (CG) algorithm with warm starting is exploited in [12 ] to solve this subproblem. However, applying CG algorithm is also time consuming.…”

Section: Fadmm‐based Approach For Isar Imagingmentioning

confidence: 99%

“…For example, authors in [12 ], exploit ADMM to solve the optimisation problem of joint SAR imaging and multi‐feature decomposition. The main drawback of this method is the high computational complexity of the algorithm due to the computation of matrix inversion [12 ]. Therefore, in this Letter, a fast ADMM (FADMM) algorithm for ISAR imaging is proposed to accelerate the image reconstruction.…”

Section: Introductionmentioning

confidence: 99%

“…More recently, ADMM has been used in synthetic aperture radar (SAR) imaging. For example, authors in [12 ], exploit ADMM to solve the optimisation problem of joint SAR imaging and multi‐feature decomposition. The main drawback of this method is the high computational complexity of the algorithm due to the computation of matrix inversion [12 ].…”

Section: Introductionmentioning

confidence: 99%

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Fast ADMM-based approach for high-resolution ISAR imaging

Hashempour

2020

Electronics Letters

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Due to the sparse nature of inverse synthetic aperture radar (ISAR) images, sparsity‐driven algorithms have been developed for high‐resolution ISAR imaging. Recently, alternating direction method of multipliers (ADMMs) has been introduced for sparse signal recovery. In this Letter, based on ADMM, a fast algorithm for ISAR imaging is proposed which has lower computational complexity than that of conventional sparse representation‐based methods and achieves good robustness in low‐signal‐to‐noise ratio conditions. Experimental results based on both simulations and real data of the Yak‐42 airplane show the superiority of the proposed method.

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Section: Resultsmentioning

confidence: 99%

“…Note that, although (14 ) has analytical solution, the computation of matrix inverse is impractical for large values of

I

. Therefore, a conjugate‐gradient (CG) algorithm with warm starting is exploited in [12 ] to solve this subproblem. However, applying CG algorithm is also time consuming.…”

Section: Fadmm‐based Approach For Isar Imagingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fast ADMM-based approach for high-resolution ISAR imaging

Hashempour

2020

Electronics Letters

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“…RPCA has found applications in a variety of problems in imaging and image processing, such as denoising, feature extraction, and data recovery [14,26,33]. The main idea in the application of RPCA to the SAR problem is that one can identify the stationary background as the low rank component of the SAR data matrix, and the moving targets as the sparse component [3].…”

Section: Robust Principal Component Analysis For Sar Data Separationmentioning

confidence: 99%

Synthetic aperture imaging and motion estimation using tensor methods

Leibovich¹,

Papanicolaou²,

Tsogka³

2020

Preprint

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We consider a synthetic aperture imaging configuration, such as synthetic aperture radar (SAR), where we want to first separate reflections from moving targets from those coming from a stationary background, and then to image separately the moving and the stationary reflectors. For this purpose, we introduce a representation of the data as a third order tensor formed from data coming from partially overlapping sub-apertures. We then apply a tensor robust principal component analysis (TRPCA) to the tensor data which separates them into the parts coming from the stationary and moving reflectors.Images are formed with the separated data sets. Our analysis shows a distinctly improved performance of TRPCA, compared to the usual matrix case. In particular, the tensor decomposition can identify motion features that are undetectable when using the conventional motion estimation methods, including matrix RPCA. We illustrate the performance of the method with numerical simulations in the X-band radar regime.

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