多通道合成孔径雷达地面运行目标指示（synthetic aperture radar-ground moving target indicator，SAR-GMTI）系统的数据按一定方式重组并处理后，可认为是由表示地杂波的低秩矩阵、表示运动目标的稀疏矩阵和表示噪声的矩阵这3部分组成。但已有的基于矩阵分解的GMTI方法，对于强杂波和慢速目标会产生较大误差，并且也未根据实际应用情况而重新定义优化模型中的加权参数。针对这些问题，设计一个自适应加权参数和矩阵分解模型，进而提出一种新的基于低秩和一维稀疏矩阵分解的多通道SAR-GMTI方法，以提高矩阵分解的精确度。基于仿真数据和高分三号SAR卫星数据的实验结果表明，该方法可将运动目标准确提取且不包含杂波与噪声分量，在慢速运动目标提取和强杂波抑制方面也具有更好的性能。
郑慧敏
,
郑明洁
,
张振宁
,
申晓天
. 基于低秩和一维稀疏矩阵分解的多通道SAR-GMTI方法[J]. 中国科学院大学学报, 2022
, 39(2)
: 208
-216
.
DOI: 10.7523/j.ucas.2020.0003
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.
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