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.
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
.
DOI: 10.7523/j.ucas.2020.0003
[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.
