基于联合稀疏性的多视全极化HRRP目标识别方法

刘盛启; 占荣辉; 翟庆林; 欧建平; 张军

doi:10.11999/JEIT151019

基于联合稀疏性的多视全极化HRRP目标识别方法

doi: 10.11999/JEIT151019

基金项目:

国家自然科学基金(61471370, 61401479)

计量
- 文章访问数: 1481
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- 被引次数: 0
出版历程
- 收稿日期: 2015-09-09
- 修回日期: 2016-02-25
- 刊出日期: 2016-07-19

Multi-view Polarization HRRP Target Recognition Based on Joint Sparsity

Funds:

The National Natural Science Foundation of China (61471370, 61401479)

摘要

摘要: 该文考虑利用连续获取的多视全极化高分辨距离像(High Range Resolution Profile, HRRP)进行目标识别的问题。多视全极化HRRP样本包含了3个层次的先验信息：样本内各分量来自同一目标；单视内4种极化组合方式下的HRRP均对应相同的目标姿态；相同极化方式下的多视观测是相关的。为有效利用上述信息进行目标识别，该文提出一种基于联合稀疏表示的多视全极化HRRP目标识别方法。该方法约束各分量对应的稀疏表示系数共享原子级的稀疏模式。原子级稀疏约束使得从各极化字典中选择来自相同姿态的字典原子对样本中各分量进行稀疏表示，可以有效利用上述3个层次的先验信息进行目标识别。利用目标电磁散射数据对所提方法进行了验证，结果表明，该方法具有较好的识别性能，并且对噪声具有良好的鲁棒性。
- 雷达目标识别 /
- 多视 /
- 全极化 /
- 高分辨距离像 /
- 联合稀疏表示
Abstract: The issue of automatically recognizing a target from its Full-Polarization High Range Resolution Profiles (FPHRRPs) with consecutive observations is considered. The prior information contained in a multi-view FPHRRP sample is hierarchical: all the entries contained in the sample are originated from the same target; the entries within a single view are associated with the same target pose; the multiple views under the same polarization mode are correlated. To utilize efficiently the prior information for target recognition, a novel joint sparse representation based multi-view FPHRRPs target recognition method is proposed. The presented method assumes all the entries within a multi-view FPHRRP sample share a common sparsity pattern in their sparse representation vectors at atom-level, which has the advantage of exploiting the aforementioned information to enhance recognition performance. Experiments are conducted using a synthetic vehicle target dataset. The results show that the proposed method achieves promising recognition accuracy and it is robust with respect to noisy observations.
- Radar target recognition /
- Multi-view /
- Full-polarization /
- High Range Resolution Profile (HRRP) /
- Joint sparse representation

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参考文献(28)

LI Hsuehjyh and LANE Rongyuan. Utilization of multiple polarization data for aerospace target identification[J]. IEEE Transactions on Antennas and Propagation, 1995, 43(12): 1436-1441. doi: 10.1109/8.475934.

张瑞, 牛威, 寇鹏. 基于样本紧密度的雷达高分辨距离像识别方法研究[J]. 电子与信息学报, 2014, 36(3): 529-536. doi: 10.3724/SP.J.1146.2013.00616.

ZHANG Rui, NIU Wei, and KOU Peng. Radar high resolution range profiles recognition based on the affinity[J]. Journal of Electronics Information Technology, 2014, 36(3): 529-536. doi: 10.3724/SP.J.1146.2013.00616.

WONG S K. High range resolution profiles as motion- Invariant features for moving ground targets identification in SAR-based automatic target recognition[J]. IEEE Transactions on Aerospace and Electronic Systems, 2009, 45(3): 1017-1039. doi: 10.1109/TAES.2009.525 9180.

SHAW K, PAUL A S, and WILLAMS R. Eigen-template- based HRR-ATR with multi-look and time-recursion[J]. IEEE Transactions on Aerospace and Electronic Systems, 2013, 49(4): 2369-2385. doi: 10.1109/TAES.2013.6621822.

冯博, 陈渤, 王鹏辉, 等. 利用稳健字典学习的雷达高分辨距离像目标识别算法[J]. 电子与信息学报, 2015, 37(6): 1457-1462. doi: 10.11999/JEIT141227.

FENG Bo, CHEN Bo, WANG Penghui, et al. Radar high resolution range profile target recognition algorithm via stable dictionary learning[J]. Journal of Electronics Information Technology, 2015, 37(6): 1457-1462. doi: 10.11999/JEIT141227.

MA Xiaoshuang, SHEN Huangfeng, YANG Jie, et al. Polarimetric-spatial classification of SAR images based on the fusion of multiple classifiers[J]. IEEE Journal of Selected Topic in Applied Earth Observation and Remote Sensing, 2014, 7(3): 961-971. doi: 10.1109/JSARS2013.2265331.

ZHANG Hongsheng, LIN Hui, and LI Yu. Impacts of feature normalization on optical and SAR data fusion for land use/land cover classification[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(5): 1061-1065. doi: 10.1109/LGRS.2014.2377722.

CARMINE C, LUCA P, IAN P, et al. Pseudo-Zernike-based multi-pass automatic target recognition from multi-channel synthetic aperture radar[J]. IET Radar, Sonar Navigation, 2015, 9(4): 457-466. doi: 10.1049/iet-rsn.2014.0296.

张玉玺, 王晓丹, 姚旭, 等. 基于H/A/a分解的全极化HRRP目标识别方法[J]. 系统工程与电子技术, 2013, 35(12): 2501-2507. doi: 10.3969/j.issn.1001-506X.2013.12.10.

ZHANG Yuxi, WANG Xiaodan, YAO Xu, et al. Target recognition of fully polarimetric HRRP based on H/A/a decomposition[J]. Systems Engineering and Electronics, 2013, 35(12): 2501-2507. doi: 10.3969/j.issn.1001-506X.2013.12.10.

张玉玺, 王晓丹, 姚旭, 等. 基于复数全极化HRRP的雷达目标识别[J]. 系统工程与电子技术, 2014, 36(2): 260-265. doi: 10.3969/j.issn.1001-506X.2014.02.10.

ZHANG Yuxi, WANG Xiaodan, YAO Xu, et al. Radar target recognition based on complex fully polarimetric HRRP[J]. Systems Engineering and Electronics, 2014, 36(2): 260-265. doi: 10.3969 /j.issn.1001-506X.2014.02.10.

LIU Shengqi, ZHAN Ronghui, ZHAI Qinglin, et al. Multi-view radar target recognition based on multitask compressive sensing[J]. Journal of Electromagnetic Waves and Applications, 2015, 29(14): 1917-1934. doi: 10.1080/ 09205071.2015.1067647.

CUI Minshan, SAURABH P, MAJID M, et al. Decision fusion of textural features derived from polarimetric data for levee assessment[J]. IEEE Journal of Selected Topic in Applied Earth Observation and Remote Sensing, 2012, 5(3): 970-976. doi: 10.1109/JSTARS.2012. 2195713.

DENNIS M B and PAUL G. A target identification comparison of Bayesian and Dempster-Shafer multisensor fusion[J]. IEEE Transactions on Systems, Man, and CyberneticsPart A: Systems and Humans, 1997, 27(5): 569-577. doi: 10.1109/3468.618256.

DONG Ganggang and KUANG Gangyao. Target recognition via information aggregation through Dempster-Shafers evidence theory[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(6): 1247-1251. doi: 10.1109/LGRS.2015.- 2390914.

XIAO Jiangjian and SHAH M. Automatic target recognition using multi-view morphing[C]. Proceedings of SPIE, Automatic Target Recognition XIV, Orlando, FL, United States, 2004: 391-399. doi: 10.1117/12.540892.

YUAN Xiaotong, LIU Xiaobai, and Yan Shuicheng. Visual classification with multitask joint sparse representation[J]. IEEE Transactions on Image Processing, 2012, 21(10): 4349-4360. doi: 10.1109/TIP.2012.2205006.

ZHANG Haichao, NASSER M N, ZHANG Yanning, et al. Multi-view automatic target recognition using joint sparse representation[J]. IEEE Transactions on Aerospace and Electronic Systems, 2012, 48(3): 2481-2497. doi: 10.1109/TAES.2012.6237604.

TROPP J A, GILBERT A C, and STRAUSS M J. Algorithms for simultaneous sparse approximation Part I: greedy pursuit[J]. EURASIP Journal on Applied Signal Processing, 2006, 86(3): 589-602. doi: 10.1016/j.sigpro.2005.05.030.

RAKOTOMAMONJY A. Surveying and comparing simultaneous sparse approximation (or group-lasso) algorithms[J]. Signal Processing, 2011, 91(7): 1505-1526. doi: 10.1016/j.sigpro.2011.01.012.

JOHN W, YANG A Y, GANESH A, et al. Robust face recognition via sparse representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(2): 210-227. doi: 10.1109/TPAMI.2008.79.

CEDRIC L, FRISON P L, CELINE T, et al. Support vector machine for multifrequency SAR polarimetric data classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(12), 4143-4152. doi: 10.1109/TGRS. 2009.2023908.

ZHANG Haichao, NASSER M N, ZHANG Yanning, et al. Joint dynamic sparse representation for multi-view face recognition[J]. Pattern Recognition, 2012, 45(4), 1290-1298. doi: 10.1016/j.patcog.2011.09.009.

Kontt E F. A progression of high frequency RCS prediction techniques[J]. Proceedings of the IEEE, 1985, 73(2): 252-264. doi: 10.1109/PROC.1985.13137.

LIU Shengqi, ZHAN Ronghui, ZHANG Jun, et al. Radar automatic target recognition based on sequential vanishing component analysis[J]. Progress In Electromagnetics Research, 2014, 145: 241-250. doi: 10.2528/PIER14011608.

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