联合图形约束和稳健主成分分析的地面动目标检测算法

郭小路; 陶海红; 杨东

doi:10.11999/JEIT151462

联合图形约束和稳健主成分分析的地面动目标检测算法

doi: 10.11999/JEIT151462

基金项目:

国家自然科学基金(60971108)，西安电子科技大学基本科研业务费资助项目(BDY061428)

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出版历程
- 收稿日期: 2015-12-24
- 修回日期: 2016-05-23
- 刊出日期: 2016-10-19

Ground Moving Target Detection Based on Robust Principal Component Analysis and Shape Constraint

Funds:

The National Natural Science Foundation of China (60971108), Xidian University Foundation (BDY061428)

摘要

摘要: 地面动目标检测是多通道合成孔径雷达系统的重要应用。稳健主成分分析的方法，因其可以将矩阵中低秩分量、稀疏分量及噪声分量分离的特性，而在多个领域得到了广泛应用。然而，该方法受到非理想误差影响，使得动目标检测结果中存在大量的杂波扰动点，从而影响动目标的检测性能。针对这一问题，该文提出一种联合稳健主成分分析和图形约束的动目标检测算法，结合系统参数对动目标区域进行形状约束，有效保证动目标检测的同时去除杂波扰动点。仿真和实测数据验证了该算法在强杂波背景下对动目标检测的有效性和可行性。
- 合成孔径雷达 /
- 地面动目标检测 /
- 主成分分析 /
- 形状约束
Abstract: Ground moving target detection is a major application in multichannel Synthetic Aperture Radar (SAR) system. In recent years, method based on Robust Principal Component Analysis (RPCA) has attracted much attention for its good performance in distinguishing the difference among a set of correlative database. However, this kind of method might be disturbed by strong clutter points since some non-ideal factors exist. Therefore, a combined RPCA shape constraint based algorithm for moving target detection is proposed in this paper. By estimating the shape information of the moving target with system parameters, the moving target would be effectively detected, and the disturbed points would be removed at the same time. The experimental data demonstrate its good performance to detect motive target under the strong clutter background.
- SAR /
- Ground moving target detection /
- Principal Component Analysis (PCA) /
- Shape constraint

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参考文献(18)

WARD J. Space-time adaptive processing for airborne radar [C]. 1995 International Conference on Acoustics, Speech, and Signal Processing, Lexington, MA, 1995: 2809-2812. doi: 10.1049/ic:19980240.

KLEMM R. Introduction to space-time adaptive processing[J]. Electronics Communication Engineering Journal, 1999, 11: 108-111. doi: 10.1049/ecej:19990102.

SJOGREN T K, VU V T, PETTERSSON M I, et al. Suppression of clutter in multichannel SAR GMTI[J]. IEEE Transactions on Geoscience Remote Sensing, 2014, 52(7): 4005-4013. doi: 10.1109/TGRS.2013.2278701.

RICHARDSON P G. STAP covariance matrix structure and its impact on clutter plus jamming suppression solutions[J]. Electronics Letters, 2001, 37(2): 118-119. doi: 10.1049/el: 20010090.

WANG Y, CHEN J W, BAO Z, et al. Robust space-time adaptive processing for airborne radar[J]. IEEE Transactions on Aerospace Electronic Systems, 2003, 39(1): 70-81. doi: 10.1109/TAES.2003.1188894.

WEINER D D, CAPRARO G T, and WICKS M C. An approach for utilizing known terrain and land feature data in estimation of the clutter covariance matrix[C]. IEEE International Radar Conference, Dallas, USA, 1998: 381-386. doi: 10.1109/NRC.1998.678032.

GUO B, VU D, XU L, et al. Ground moving target indication via multichannel airborne SAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(10): 3753-3764. doi: 10.1109/TGRS.2011.2143420.

CANDES E J, LI X, MA Y, et al. Robust principal component analysis[J]. Journal of ACM, 2009, 8(1): 1-73.

BUGEAU A and PEREZ P. Detection and segmentation of moving objects in complex scenes[J]. Computer Vision and Image Understanding, 2009, 113(4): 459-476. doi: 10.1016/ j.cviu.2008.11.005.

DANG C and RADHA H. RPCA-KFE: key frame extraction for video using robust principal component analysis[J]. IEEE Transactions on Image Processing, 2015, 24(11): 3742-3753. doi: 10.1109/TIP.2015.2445572.

JAVED S, BOUWMANS T, and JUNG S K. Depth extended online RPCA with spatiotemporal constraints for robust background subtraction[C]. Workshop on Frontiers of Computer Vision, 2015: 1-6. doi: 10.1109/FCV.2015. 7103745.

KHAJI R, LI H, LI H F, et al. Improved combination of RPCA and MEL for sparse representation-based face recognition[J]. International Journal of Wavelets Multiresolution Information Processing, 2014, 12(3): 911-922. doi: 10.1142/S0219691314500313.

LUAN X, FANG B, LIU L H, et al. Extracting sparse error of robust PCA for face recognition in the presence of varying illumination and occlusion[J]. Pattern Recognition, 2014, 47(2): 495-508. doi: 10.1016/j.patcog.2013.06.031.

ELONS A S, AHMED M, and SHEDID H. Facial expressions recognition for Arabic sign language translation[C]. 2014 9th International Conference on Computer Engineering Systems (ICCES), Cairo, Egypt, 2014: 330-335. doi: 10.1109/ ICCES.2014.7030980

HEIMAN E, SCHECHATMAN G, and SHRAIBMAN A. Deterministic algorithms for matrix completion[J]. Random Structures Algorithms, 2014, 45(2): 306-317. doi: 10.1002/rsa.20483.

CANDES E J and PLAN Y. Matrix completion with noise[J]. Proceedings of the IEEE, 2010, 98(6): 925-936. doi: 10.1109/ JPROC.2009.2035722.

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.

ZHOU T Y and TAO D C. GoDec: Randomized low-rank sparse matrix decomposition in noisy case[C]. International Conference on Machine Learning, Washington, USA, 2011: 33-40.

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