A Fast Sparse Recovery Algorithm via Resolution Approximation for LASAR 3D Imaging

Tian, Bokun; Wei, Shunjun; Ming, Jing

doi:10.1109/access.2019.2959128

Cited by 9 publications

(6 citation statements)

References 40 publications

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“…In addition, the OMP [17], fast marginalized sparse Bayesian learning (FMSBL) [29,30], and SBRIM [21] algorithms are used as the comparison algorithms to evaluate the performance of the FBCS-RVM algorithm better. The normalized mean square error (NMSE) [31,32] measures the scattering coefficients' estimation accuracy; the smaller NMSE indicates that the estimation results of the scattering coefficients are more accurate.…”

Section: Results On Simulation and Experimental Datamentioning

confidence: 99%

“…Therefore, the larger TBR and smaller ENT indicate higher imaging quality. The running time speed-up ratio (RTSR) [21] and execution time (ExT) are used to evaluate the computational efficiency. The higher RTSR indicates that the improvement of the computational efficiency is larger between the FBCS-RVM and the comparison algorithm.…”

Section: Results On Simulation and Experimental Datamentioning

confidence: 99%

“…Once the target-areas G n have several elements whose scattering coefficients are too small or closer to zero compared to the other elements, the diagonal matrix Θ Λ t is singular and irreversible because of the elements with scattering coefficients that are too small; we cannot estimate α(t) w correctly by Equation (21). To eliminate the elements with a scattering coefficient that is too small in G n , the truncated singular value decomposition (TSVD) algorithm is introduced in this subsection.…”

Section: Sparse Imaging According To the Target-areasmentioning

confidence: 99%

“…Therefore, the computational efficiency of sparse imaging can be improved effectively by simplifying the measurement matrix. In our previous research, the fast sparse recovery algorithm via resolution approximation (FSRARA) [21] improves the computational efficiency of sparse imaging successfully through simplifying the measurement matrix according to the targetareas in the imaging scene. However, once the 3D preliminary imaging results are not obtained correctly, the FSRARA cannot extract the target-areas accurately and estimate the scattering coefficients correctly.…”

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confidence: 99%

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Fast Bayesian Compressed Sensing Algorithm via Relevance Vector Machine for LASAR 3D Imaging

Tian

et al. 2021

Remote Sensing

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Because of the three-dimensional (3D) imaging scene’s sparsity, compressed sensing (CS) algorithms can be used for linear array synthetic aperture radar (LASAR) 3D sparse imaging. CS algorithms usually achieve high-quality sparse imaging at the expense of computational efficiency. To solve this problem, a fast Bayesian compressed sensing algorithm via relevance vector machine (FBCS–RVM) is proposed in this paper. The proposed method calculates the maximum marginal likelihood function under the framework of the RVM to obtain the optimal hyper-parameters; the scattering units corresponding to the non-zero optimal hyper-parameters are extracted as the target-areas in the imaging scene. Then, based on the target-areas, we simplify the measurement matrix and conduct sparse imaging. In addition, under low signal to noise ratio (SNR), low sampling rate, or high sparsity, the target-areas cannot always be extracted accurately, which probably contain several elements whose scattering coefficients are too small and closer to 0 compared to other elements. Those elements probably make the diagonal matrix singular and irreversible; the scattering coefficients cannot be estimated correctly. To solve this problem, the inverse matrix of the singular matrix is replaced with the generalized inverse matrix obtained by the truncated singular value decomposition (TSVD) algorithm to estimate the scattering coefficients correctly. Based on the rank of the singular matrix, those elements with small scattering coefficients are extracted and eliminated to obtain more accurate target-areas. Both simulation and experimental results show that the proposed method can improve the computational efficiency and imaging quality of LASAR 3D imaging compared with the state-of-the-art CS-based methods.

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Section: Results On Simulation and Experimental Datamentioning

confidence: 99%

Section: Results On Simulation and Experimental Datamentioning

confidence: 99%

Section: Sparse Imaging According To the Target-areasmentioning

confidence: 99%

mentioning

confidence: 99%

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Fast Bayesian Compressed Sensing Algorithm via Relevance Vector Machine for LASAR 3D Imaging

Tian

et al. 2021

Remote Sensing

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“…Synthetic Aperture Radars (SAR) that are mounted on aircrafts, satellites or other platforms are usually used to obtain images of regions of interest for all-weather all-time high-resolution reconnaissance [1][2][3]. In recent years, many SAR system such as bi-static (multi-static) SAR, linear array SAR, three dimension SAR and frequency-modulated continuous-wave (FMCW) SAR have been designed to obtain SAR data [4][5][6][7][8][9][10], and many techniques such as displacement phase center antenna (DPCA), differential interferometry, along-track interferometry, space time adaptive processing (STAP), adaptive digital beam forming and phase unwrapping have been employed to process SAR data [11][12][13][14][15][16][17]. However, ground moving target imaging is still a challenging task due to the unknown target's trajectory.…”

Section: Introductionmentioning

confidence: 99%

Ground Moving Target Tracking and Refocusing Using Shadow in Video-SAR

et al. 2020

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Stable and efficient ground moving target tracking and refocusing is a hard task in synthetic aperture radar (SAR) data processing. Since shadows in video-SAR indicate the actual positions of moving targets at different moments without any displacement, shadow-based methods provide a new approach for ground moving target processing. This paper constructs a novel framework to refocus ground moving targets by using shadows in video-SAR. To this end, an automatic-registered SAR video is first obtained using the video-SAR back-projection (v-BP) algorithm. The shadows of multiple moving targets are then tracked using a learning-based tracker, and the moving targets are ultimately refocused via a proposed moving target back-projection (m-BP) algorithm. With this framework, we can perform detecting, tracking, imaging for multiple moving targets integratedly, which significantly improves the ability of moving-target surveillance for SAR systems. Furthermore, a detailed explanation of the shadow of a moving target is presented herein. We find that the shadow of ground moving targets is affected by a target’s size, radar pitch angle, carrier frequency, synthetic aperture time, etc. With an elaborate system design, we can obtain a clear shadow of moving targets even in X or C band. By numerical experiments, we find that a deep network, such as SiamFc, can easily track shadows and precisely estimate the trajectories that meet the accuracy requirement of the trajectories for m-BP.

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A joint sparse recovery algorithm for coprime adjacent array synthetic aperture radar 3D sparse imaging

Tian

Tang

Shi

2021

International Journal of Remote Sensing

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A Fast Sparse Recovery Algorithm via Resolution Approximation for LASAR 3D Imaging

Cited by 9 publications

References 40 publications

Fast Bayesian Compressed Sensing Algorithm via Relevance Vector Machine for LASAR 3D Imaging

Fast Bayesian Compressed Sensing Algorithm via Relevance Vector Machine for LASAR 3D Imaging

Ground Moving Target Tracking and Refocusing Using Shadow in Video-SAR

A joint sparse recovery algorithm for coprime adjacent array synthetic aperture radar 3D sparse imaging

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