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

GUO Xiaolu; TAO Haihong; YANG Dong

doi:10.11999/JEIT151462

Volume 38 Issue 10

Oct. 2016

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(10): 2475-2481

GUO Xiaolu, TAO Haihong, YANG Dong. Ground Moving Target Detection Based on Robust Principal Component Analysis and Shape Constraint[J]. Journal of Electronics & Information Technology, 2016, 38(10): 2475-2481. doi: 10.11999/JEIT151462

Citation:

GUO Xiaolu, TAO Haihong, YANG Dong. Ground Moving Target Detection Based on Robust Principal Component Analysis and Shape Constraint[J]. Journal of Electronics & Information Technology, 2016, 38(10): 2475-2481. doi: 10.11999/JEIT151462

Citation:

PDF( 2686 KB)

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

doi: 10.11999/JEIT151462

Funds:

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

Received Date: 2015-12-24
Rev Recd Date: 2016-05-23
Publish Date: 2016-10-19

Abstract

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

FullText(HTML)

References(18)

References

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.

Relative Articles

Supplements(0)

Cited By

Proportional views