A multichannel SAR-GMTI method based on low-rank and one-dimensional sparse matrix decomposition

doi:10.7523/j.ucas.2020.0003

Abstract

Abstract: After being regrouped and processed, the data of the multichannel SAR-GMTI (synthetic aperture radar-ground moving target indicator) system can be considered as a joint matrix composed of three matrices, namely, a low-rank matrix of ground clutter, a sparse matrix of moving targets, and an entry-wise matrix of noise component. The existing GMTI method based on matrix decomposition can cause error by the impact of the strong clutter or slow-moving targets. Moreover, the weighted parameters in the optimization model are not redefined according to the actual application. To solve these problems, an adaptive weighted parameter and matrix decomposition model are designed in this paper, and a new multi-channel SAR-GMTI method based on low-rank and one-dimensional sparse matrix decomposition is proposed to improve the accuracy of matrix decomposition. The results based on the simulation data and the real data from Gaofen-3 SAR satellite demonstrate that the proposed method can accurately extract moving targets without clutter and noise components, and can obtain better performance in slow-moving target detection and strong clutter suppression too.

Key words: multichannel synthetic aperture radar(SAR), ground moving-target indication(GMTI), low-rank and sparse matrix decomposition

CLC Number:

TN957.51

ZHENG Huimin, ZHENG Mingjie, ZHANG Zhenning, SHEN Xiaotian. A multichannel SAR-GMTI method based on low-rank and one-dimensional sparse matrix decomposition[J]. Journal of University of Chinese Academy of Sciences, 2022, 39(2): 208-216.

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