Fast Bayesian Compressed Sensing Algorithm via Relevance Vector Machine for LASAR 3D Imaging
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 prob...
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doaj-05b3cd0e4a614bf68c44c9ad4e4c91642021-04-30T23:06:30ZengMDPI AGRemote Sensing2072-42922021-04-01131751175110.3390/rs13091751Fast Bayesian Compressed Sensing Algorithm via Relevance Vector Machine for LASAR 3D ImagingBokun Tian0Xiaoling Zhang1Liang Li2Ling Pu3Liming Pu4Jun Shi5Shunjun Wei6School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaBecause 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.https://www.mdpi.com/2072-4292/13/9/1751linear array synthetic aperture radar (LASAR)compressed sensing (CS)fast Bayesian compressed sensing algorithm via relevance vector machine (FBCS–RVM)three-dimensional (3D) imaginghigh computational efficiency |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Bokun Tian Xiaoling Zhang Liang Li Ling Pu Liming Pu Jun Shi Shunjun Wei |
spellingShingle |
Bokun Tian Xiaoling Zhang Liang Li Ling Pu Liming Pu Jun Shi Shunjun Wei Fast Bayesian Compressed Sensing Algorithm via Relevance Vector Machine for LASAR 3D Imaging Remote Sensing linear array synthetic aperture radar (LASAR) compressed sensing (CS) fast Bayesian compressed sensing algorithm via relevance vector machine (FBCS–RVM) three-dimensional (3D) imaging high computational efficiency |
author_facet |
Bokun Tian Xiaoling Zhang Liang Li Ling Pu Liming Pu Jun Shi Shunjun Wei |
author_sort |
Bokun Tian |
title |
Fast Bayesian Compressed Sensing Algorithm via Relevance Vector Machine for LASAR 3D Imaging |
title_short |
Fast Bayesian Compressed Sensing Algorithm via Relevance Vector Machine for LASAR 3D Imaging |
title_full |
Fast Bayesian Compressed Sensing Algorithm via Relevance Vector Machine for LASAR 3D Imaging |
title_fullStr |
Fast Bayesian Compressed Sensing Algorithm via Relevance Vector Machine for LASAR 3D Imaging |
title_full_unstemmed |
Fast Bayesian Compressed Sensing Algorithm via Relevance Vector Machine for LASAR 3D Imaging |
title_sort |
fast bayesian compressed sensing algorithm via relevance vector machine for lasar 3d imaging |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2021-04-01 |
description |
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. |
topic |
linear array synthetic aperture radar (LASAR) compressed sensing (CS) fast Bayesian compressed sensing algorithm via relevance vector machine (FBCS–RVM) three-dimensional (3D) imaging high computational efficiency |
url |
https://www.mdpi.com/2072-4292/13/9/1751 |
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