聯(lián)合圖形約束和穩(wěn)健主成分分析的地面動(dòng)目標(biāo)檢測算法

郭小路; 陶海紅; 楊東

doi:10.11999/JEIT151462

聯(lián)合圖形約束和穩(wěn)健主成分分析的地面動(dòng)目標(biāo)檢測算法

doi: 10.11999/JEIT151462

基金項(xiàng)目:

國家自然科學(xué)基金(60971108)，西安電子科技大學(xué)基本科研業(yè)務(wù)費(fèi)資助項(xiàng)目(BDY061428)

計(jì)量
- 文章訪問數(shù): 1235
- HTML全文瀏覽量: 112
- PDF下載量: 513
- 被引次數(shù): 0
出版歷程
- 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: 地面動(dòng)目標(biāo)檢測是多通道合成孔徑雷達(dá)系統(tǒng)的重要應(yīng)用。穩(wěn)健主成分分析的方法，因其可以將矩陣中低秩分量、稀疏分量及噪聲分量分離的特性，而在多個(gè)領(lǐng)域得到了廣泛應(yīng)用。然而，該方法受到非理想誤差影響，使得動(dòng)目標(biāo)檢測結(jié)果中存在大量的雜波擾動(dòng)點(diǎn)，從而影響動(dòng)目標(biāo)的檢測性能。針對(duì)這一問題，該文提出一種聯(lián)合穩(wěn)健主成分分析和圖形約束的動(dòng)目標(biāo)檢測算法，結(jié)合系統(tǒng)參數(shù)對(duì)動(dòng)目標(biāo)區(qū)域進(jìn)行形狀約束，有效保證動(dòng)目標(biāo)檢測的同時(shí)去除雜波擾動(dòng)點(diǎn)。仿真和實(shí)測數(shù)據(jù)驗(yàn)證了該算法在強(qiáng)雜波背景下對(duì)動(dòng)目標(biāo)檢測的有效性和可行性。
- 合成孔徑雷達(dá) /
- 地面動(dòng)目標(biāo)檢測 /
- 主成分分析 /
- 形狀約束
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

HTML全文

參考文獻(xiàn)(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.

相關(guān)文章

施引文獻(xiàn)

資源附件(0)

訪問統(tǒng)計(jì)