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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Veröffentlicht in:EURASIP journal on advances in signal processing Jg. 2017; H. 1; S. 1 - 11
Hauptverfasser: Gao, Yu-Fei, Gui, Guan, Cong, Xun-Chao, Yang, Yue, Zou, Yan-Bin, Wan, Qun
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cham Springer International Publishing 12.06.2017
Springer
Springer Nature B.V
SpringerOpen
Schlagworte:
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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Zusammenfassung: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.
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ISSN:1687-6180
1687-6172
1687-6180
DOI:10.1186/s13634-017-0479-7