基于低秩和一维稀疏矩阵分解的多通道SAR-GMTI方法

doi:10.7523/j.ucas.2020.0003

中国科学院大学学报 ›› 2022, Vol. 39 ›› Issue (2): 208-216.DOI: 10.7523/j.ucas.2020.0003

• 电子信息与计算机科学 • 上一篇

基于低秩和一维稀疏矩阵分解的多通道SAR-GMTI方法

郑慧敏^1,2, 郑明洁¹, 张振宁^1,2, 申晓天^1,2

1 中国科学院空天信息创新研究院, 北京 100190;
2 中国科学院大学电子电气与通信工程学院, 北京 100049

收稿日期:2020-01-06 修回日期:2020-04-26 发布日期:2021-05-31
通讯作者: 郑明洁
基金资助:
国家重点研发计划（2017YFB0502700）资助

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

ZHENG Huimin^1,2, ZHENG Mingjie¹, ZHANG Zhenning^1,2, SHEN Xiaotian^1,2

1 Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China;
2 School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China

Received:2020-01-06 Revised:2020-04-26 Published:2021-05-31

摘要/Abstract

摘要： 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。

关键词: 多通道合成孔径雷达(SAR), 地面运动目标指示(GMTI), 低秩和稀疏矩阵分解

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

中图分类号:

TN957.51

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.

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.

参考文献

[1] 肖垚, 刘畅. 基于稀疏求解的改进PCA方法在SAR目标识别中的应用[J]. 中国科学院大学学报, 2018, 35(1):84-88.DOI:10.7523/j.issn.2095-6134.2018.01.011.
[2] 李松, 魏中浩, 张冰尘, 等.深度卷积神经网络在迁移学习模式下的SAR目标识别[J]. 中国科学院大学学报, 2018, 35(1):75-83.DOI:10.7523/j.issn.2095-6134.2018.01.010.
[3] 向卫力, 李晓辉, 周勇胜, 等. 一种鲁棒的多尺度稀疏表示SAR目标识别方法[J]. 中国科学院大学学报, 2017, 34(1):99-105.DOI:10.7523/j.issn.2095-6134.2017.01.013.
[4] Lightstone L, Faubert D, Rempel G. Multiple phase centre DPCA for airborne radar[C] //Proceedings of the 1991 IEEE National Radar Conference. March 12-13, 1991, Los Angeles, CA, USA. IEEE, 1991:36-40.DOI:10.1109/NRC.1991.114720.
[5] Cao C H, Zhang Z H, Meng J M, et al. Clutter suppression and moving target indication with airborne wide-area surveillance radar[C] //2016 CIE International Conference on Radar (RADAR). October 10-13, 2016, Guangzhou, China. IEEE, 2016:1-5.DOI:10.1109/RADAR.2016.8059385.
[6] Yan H, Li F, Robert W, et al. Moving targets extraction in multichannel wide-area surveillance system by exploiting sparse phase matrix[J]. IET Radar, Sonar & Navigation, 2012, 6(9):913-920.DOI:10.1049/iet-rsn.2012.0067.
[7] Yan H, Wang R, Li F, et al. Ground moving target extraction in a multichannel wide-area surveillance SAR/GMTI system via the relaxed PCP[J]. IEEE Geoscience and Remote Sensing Letters, 2013, 10(3):617-621.DOI:10.1109/LGRS.2012.2216248.
[8] Li Q N, Yan H, Wu L Q, et al. Robust PCA for ground moving target indication in wide-area surveillance radar system[J]. Journal of the Operations Research Society of China, 2013, 1(1):135-153.DOI:10.1007/s40305-013-0006-y.
[9] Li Q N, He L, Qi L J, et al. Unique decomposition and a new model for the ground moving target Indication problem[J]. Journal of Optimization Theory and Applications, 2017, 173(1):297-312.DOI:10.1007/s10957-016-1052-5.
[10] Lin Z C, Chen M M, Ma Y. The augmented Lagrange multiplier method for exact recovery of corrupted low-rank matrices[R/OL]. (2013-10-18) [2020-01-03]. http://arXiv.org/pdf/1009.5055.pdf.
[11] Zheng M J, Yan H, Zhang L, et al. Research on strong clutter suppression for Gaofen-3 dual-channel SAR/GMTI[J]. Sensors, 2018, 18(4):978-992.DOI:10.3390/s18040978.
[12] Guyon C, Bouwmans T, Zahzah E H. Foreground detection based on low-rank and block-sparse matrix decomposition[C]// 2012 19th IEEE International Conference on Image Processing. September 30-October 3, 2012, Orlando, FL, USA. IEEE, 2012:1225-1228.DOI:10.1109/ICIP.2012.6467087.
[13] Tang G G, Nehorai A. Robust principal component analysis based on low-rank and block-sparse matrix decomposition[C] // 2011 45th Annual Conference on Information Sciences and Systems. March 23-25, 2011, Baltimore, MD, USA. IEEE, 2011:1-5.DOI:10.1109/CISS.2011.5766144.
[14] Fadili M J, Starck J L. Monotone operator splitting for optimization problems in sparse recovery[C]//2009 16th IEEE International Conference on Image Processing (ICIP). November 7-10, 2009, Cairo, Egypt. IEEE, 2009:1461-1464.DOI:10.1109/ICIP.2009.5414555.
[15] Candès E J, Li X D, Ma Y, et al. Robust principal component analysis?[J]. Journal of the ACM, 2011, 58(3):1-37.DOI:10.1145/1970392.1970395.
[16] Hoyer P O. Non-negative matrix factorization with sparseness constraints[J]. Journal of Machine Learning Research, 2004, 5:1457-1469.

基于低秩和一维稀疏矩阵分解的多通道SAR-GMTI方法

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

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 11

编辑推荐

Metrics

本文评价

访问统计

联系我们

[1]	宋志华, 陈锟山, 曾江源, 许镇. 一种基于FMCW雷达的慢速目标检测自适应滤波器[J]. 中国科学院大学学报, 2021, 38(3): 382-391.
[2]	马娇, 董勇伟, 李原, 李凌霄, 杨杰芳. 多旋翼无人机微多普勒特性分析与特征提取[J]. 中国科学院大学学报, 2019, 36(2): 235-243.
[3]	李秋生. 常规雷达上飞机目标旋转部件回波调制特性分析[J]. 中国科学院大学学报, 2013, 30(6): 829-838.
[4]	王欣, 洪峻. 基于机载ATI-SAR相位中心误差分析的天线系统结构设计[J]. 中国科学院大学学报, 2013, 30(2): 228-232.
[5]	王静, 刘忠胜, 丁斌, 向茂生, 韦立登, 孙曦蕊. 基于PCI和FPGA的InSAR基线测量实时采集系统[J]. 中国科学院大学学报, 2013, 30(1): 53-59.
[6]	王艳霞, 张毅. 机载单天线SAR动目标检测与参数估计[J]. 中国科学院大学学报, 2012, (2): 227-233.
[7]	艾加秋, 齐向阳. 一种基于局部K-分布的新的SAR图像舰船检测算法[J]. 中国科学院大学学报, 2010, 27(1): 36-42.
[8]	张义安, 张冬晨, 陈卫东, 王东进. 基于局域波分解的机动目标ISAR成像[J]. 中国科学院大学学报, 2009, 26(4): 483-489.
[9]	苏宇, 齐向阳. 基于OpenMP的星载SAR回波信号并行仿真[J]. 中国科学院大学学报, 2008, 25(1): 129-135.
[10]	陈洁;　方广有;　李芳. 超宽带穿墙雷达非相关成像方法[J]. 中国科学院大学学报, 2007, 24(6): 829-834.
[11]	王晓林; 洪峻. 应用编码发射机在轨测量星载SAR天线方向图[J]. 中国科学院大学学报, 2006, 23(2): 186-191.