Multi-linear sparse reconstruction for SAR imaging based on higher-order SVD

This paper focuses on the spotlight synthetic aperture radar (SAR) imaging for point scattering targets based on tensor modeling. In a real-world scenario, scatterers usually distribute in the block sparse pattern. Such a distribution feature has been scarcely utilized by the previous studies of SAR...

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Published in:	EURASIP journal on advances in signal processing Vol. 2017; no. 1; pp. 1 - 11
Main Authors:	Gao, Yu-Fei, Gui, Guan, Cong, Xun-Chao, Yang, Yue, Zou, Yan-Bin, Wan, Qun
Format:	Journal Article
Language:	English
Published:	Cham Springer International Publishing 12.06.2017 Springer Springer Nature B.V SpringerOpen
Subjects:	Algorithms Alternating least squares Artificial satellites in remote sensing Complexity Compressed sensing Computer simulation Consumption (Economics) Engineering HOSVD Image reconstruction Kronecker constraint Mathematical models Noise prediction Quantum Information Technology Radar imaging Radar scattering Signal,Image and Speech Processing Singular value decomposition Spintronics Spotlight SAR imaging Synthetic aperture radar HOSVD Alternating least squares Spotlight SAR imaging Kronecker constraint Compressed sensing
ISSN:	1687-6180, 1687-6172, 1687-6180
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Abstract	This paper focuses on the spotlight synthetic aperture radar (SAR) imaging for point scattering targets based on tensor modeling. In a real-world scenario, scatterers usually distribute in the block sparse pattern. Such a distribution feature has been scarcely utilized by the previous studies of SAR imaging. Our work takes advantage of this structure property of the target scene, constructing a multi-linear sparse reconstruction algorithm for SAR imaging. The multi-linear block sparsity is introduced into higher-order singular value decomposition (SVD) with a dictionary constructing procedure by this research. The simulation experiments for ideal point targets show the robustness of the proposed algorithm to the noise and sidelobe disturbance which always influence the imaging quality of the conventional methods. The computational resources requirement is further investigated in this paper. As a consequence of the algorithm complexity analysis, the present method possesses the superiority on resource consumption compared with the classic matching pursuit method. The imaging implementations for practical measured data also demonstrate the effectiveness of the algorithm developed in this paper.
AbstractList	Abstract This paper focuses on the spotlight synthetic aperture radar (SAR) imaging for point scattering targets based on tensor modeling. In a real-world scenario, scatterers usually distribute in the block sparse pattern. Such a distribution feature has been scarcely utilized by the previous studies of SAR imaging. Our work takes advantage of this structure property of the target scene, constructing a multi-linear sparse reconstruction algorithm for SAR imaging. The multi-linear block sparsity is introduced into higher-order singular value decomposition (SVD) with a dictionary constructing procedure by this research. The simulation experiments for ideal point targets show the robustness of the proposed algorithm to the noise and sidelobe disturbance which always influence the imaging quality of the conventional methods. The computational resources requirement is further investigated in this paper. As a consequence of the algorithm complexity analysis, the present method possesses the superiority on resource consumption compared with the classic matching pursuit method. The imaging implementations for practical measured data also demonstrate the effectiveness of the algorithm developed in this paper. This paper focuses on the spotlight synthetic aperture radar (SAR) imaging for point scattering targets based on tensor modeling. In a real-world scenario, scatterers usually distribute in the block sparse pattern. Such a distribution feature has been scarcely utilized by the previous studies of SAR imaging. Our work takes advantage of this structure property of the target scene, constructing a multi-linear sparse reconstruction algorithm for SAR imaging. The multi-linear block sparsity is introduced into higher-order singular value decomposition (SVD) with a dictionary constructing procedure by this research. The simulation experiments for ideal point targets show the robustness of the proposed algorithm to the noise and sidelobe disturbance which always influence the imaging quality of the conventional methods. The computational resources requirement is further investigated in this paper. As a consequence of the algorithm complexity analysis, the present method possesses the superiority on resource consumption compared with the classic matching pursuit method. The imaging implementations for practical measured data also demonstrate the effectiveness of the algorithm developed in this paper.
ArticleNumber	44
Audience	Academic
Author	Wan, Qun Cong, Xun-Chao Gao, Yu-Fei Yang, Yue Gui, Guan Zou, Yan-Bin
Author_xml	– sequence: 1 givenname: Yu-Fei surname: Gao fullname: Gao, Yu-Fei organization: School of Electronic Engineering, University of Electronic Science and Technology of China – sequence: 2 givenname: Guan orcidid: 0000-0003-3888-2881 surname: Gui fullname: Gui, Guan organization: College of Telecommunication and Information Engineering, Nanjing University of Posts and Telecommunications – sequence: 3 givenname: Xun-Chao surname: Cong fullname: Cong, Xun-Chao organization: School of Electronic Engineering, University of Electronic Science and Technology of China – sequence: 4 givenname: Yue surname: Yang fullname: Yang, Yue organization: School of Electronic Engineering, University of Electronic Science and Technology of China – sequence: 5 givenname: Yan-Bin surname: Zou fullname: Zou, Yan-Bin organization: School of Electronic Engineering, University of Electronic Science and Technology of China – sequence: 6 givenname: Qun surname: Wan fullname: Wan, Qun email: wanqun@uestc.edu.cn organization: School of Electronic Engineering, University of Electronic Science and Technology of China
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Snippet	This paper focuses on the spotlight synthetic aperture radar (SAR) imaging for point scattering targets based on tensor modeling. In a real-world scenario,... Abstract This paper focuses on the spotlight synthetic aperture radar (SAR) imaging for point scattering targets based on tensor modeling. In a real-world...
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SubjectTerms	Algorithms Alternating least squares Artificial satellites in remote sensing Complexity Compressed sensing Computer simulation Consumption (Economics) Engineering HOSVD Image reconstruction Kronecker constraint Mathematical models Noise prediction Quantum Information Technology Radar imaging Radar scattering Signal,Image and Speech Processing Singular value decomposition Spintronics Spotlight SAR imaging Synthetic aperture radar
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Title	Multi-linear sparse reconstruction for SAR imaging based on higher-order SVD
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