Compressive Radar Imaging of Stationary Indoor Targets With Low-Rank Plus Jointly Sparse and Total Variation Regularizations

Tang, Van Ha; Bouzerdoum, A.; Phung, Son Lam

doi:10.1109/tip.2020.2973819

Cited by 17 publications

(18 citation statements)

References 50 publications

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“…holds for all k sparse signals with ∥ ∥ 0 ≤ . Formula (8) indicates that nearly all the random matrices, whose entries are drawn from Gaussian, sub-Gaussian, or Bernoulli distribution with measurement number on the order of ∥ ∥ 0 ≤ , are very likely to satisfy the D-RIP, which exploits the nearly mutually orthogonal of the columns of the sensing matrix.…”

Section: Figure 1 the Cs Process And Its Domainsmentioning

confidence: 99%

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Design and Application of a Greedy Pursuit Algorithm Adapted to Overcomplete Dictionary for Sparse Signal Recovery

Zhao¹,

Zhu²,

Wu³

2020

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Compressive sensing (CS) is a novel paradigm to recover a sparse signal in compressed domain. In some overcomplete dictionaries, most practical signals are sparse rather than orthonormal. Signal space greedy method can derive the optimal or near-optimal projections, making it possible to identify a few most relevant dictionary atoms of an arbitrary signal. More practically, such projections can be processed by standard CS recovery algorithms. This paper proposes a signal space subspace pursuit (SSSP) method to compute spare signal representations with overcomplete dictionaries, whenever the sensing matrix satisfies the restricted isometry property adapted to dictionary (D-RIP). Specifically, theoretical guarantees were provided to recover the signals from their measurements with overwhelming probability, as long as the sensing matrix satisfies the D-RIP. In addition, a thorough analysis was performed to minimize the number of measurements required for such guarantees. Simulation results demonstrate the validity of our hypothetical theory, as well as the superiority of the proposed approach.

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Section: Figure 1 the Cs Process And Its Domainsmentioning

confidence: 99%

“…Compressive sensing (CS) [1,2] is a novel paradigm that recovers signals that are sparse in a certain domain, from a small set of compressed measurements. This paradigm has been widely applied in various signal processing applications [3][4][5][6][7][8], ranging from image [9,10], audio [11][12][13], to video [14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Design and Application of a Greedy Pursuit Algorithm Adapted to Overcomplete Dictionary for Sparse Signal Recovery

Zhao¹,

Zhu²,

Wu³

2020

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“…Total variation (TV) method, which introduces the gradient constraint of targets, can effectively preserve the contour of targets. Recently, TV method has been widely used in imaging restor and radar imaging [12][13][14]. In [15], we proposed a one-dimensional TV method to improve the azimuth resolution of airborne radar.…”

Section: Introductionmentioning

confidence: 99%

Airborne Radar Super-Resolution Imaging Based on Fast Total Variation Method

Zhang

Huang

et al. 2021

Remote Sensing

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Total variation (TV) is an effective super-resolution method to improve the azimuth resolution and preserve the contour information of the target in airborne radar imaging. However, the computational complexity is very high because of the matrix inversion, reaching O(N3). In this paper, a Gohberg–Semencul (GS) representation based fast TV (GSFTV) method is proposed to make up for the shortcoming. The proposed GSFTV method fist utilizes a one-dimensional TV norm as the regular term under regularization framework, which is conducive to achieve super-resolution while preserving the target contour. Then, aiming at the very high computational complexity caused by matrix inversion when minimizing the TV regularization problem, we use the low displacement rank feature of Toeplitz matrix to achieve fast inversion through GS representation. This reduces the computational complexity from O(N3) to O(N2), benefiting efficiency improvement for airborne radar imaging. Finally, the simulation and real data processing results demonstrate that the proposed GSFTV method can simultaneously improve the resolution and preserve the target contour. Moreover, the very high computational efficiency of the proposed GSFTV method is tested by hardware platform.

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“…To reduce the measurement data amount and still providing high‐resolution imaging, compressive sensing (CS) and sparse reconstruction techniques have been applied to TWR systems [4, 5 ]. However, the combination of impulse TWR Stolt migration imaging and CS has never been considered in the literature before.…”

Section: Introductionmentioning

confidence: 99%

Compressive sensing‐based Stolt migration imaging algorithm for impulse through‐the‐wall radar

Fathy

2020

Electron. lett.

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In this Letter, the authors propose using compressive sensing (CS)‐based Stolt migration imaging approach to reduce the amount of measurement data and provide an enhanced imaging quality. The authors utilise both forward and backward Stolt migration imaging operators to deal with the measurement and image the space data in two‐dimensional matrix form for sparse recovery computation. This imaging process accounts for the electromagnetic wave propagation and does not require prior knowledge of the transmitted pulse, which is practically not available in practice. Results of the proposed numerical simulation demonstrate the validity and effectiveness of the proposed imaging algorithm.

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Compressive Radar Imaging of Stationary Indoor Targets With Low-Rank Plus Jointly Sparse and Total Variation Regularizations

Cited by 17 publications

References 50 publications

Design and Application of a Greedy Pursuit Algorithm Adapted to Overcomplete Dictionary for Sparse Signal Recovery

Design and Application of a Greedy Pursuit Algorithm Adapted to Overcomplete Dictionary for Sparse Signal Recovery

Airborne Radar Super-Resolution Imaging Based on Fast Total Variation Method

Compressive sensing‐based Stolt migration imaging algorithm for impulse through‐the‐wall radar

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