Making full and effective use of target polarization information from High Resolution Range Profile (HRRP) is a hot issue for improving the recognition performance of maritime surveillance radar. A HRRP database with seven maritime targets classes from various aspect angles is established, on which thirty-nine features from four categories are defined. A novel feature selection method based on the Normalized Mutual Information (NMI) and Simulated Annealing (SA) algorithm is presented, named as NMI-SA. The effectiveness of the NMI-SA is proved by comparison with three other methods using HRRP dataset and eight from UCI machine learning repository. Finally, the NMI-SA is applied to the HRRP dataset to find twenty-five high discriminant and low redundancy features.
范学满,胡生亮,贺静波. 对海雷达目标识别中全极化HRRP的特征提取与选择[J]. 电子与信息学报, 2016, 38(12): 3261-3268.
FAN Xueman, HU Shengliang, HE Jingbo. Feature Extraction and Selection of Full Polarization HRRP in Target Recognition Process of Maritime Surveillance Radar. JEIT, 2016, 38(12): 3261-3268.
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.
GUO Zunhua, LI Da, and ZHANG Boyan. Survey of radar target recognition using one-dimensional high range resolution profiles[J]. Systems Engineering and Electronics, 2013, 35(1): 53-60. doi: 10.3969/j.issn.1001-506X.2013.01.09.
[3]
PICHER C and KHOTANZAD A. Nonlinear classifier combination for a maritime target recognition task[C]. Proceedings of the IEEE Radar Conference, Pasadena, 2009: 873-877. doi: 10.1109/RADAR.2009.4976923.
LIU Shengqi, ZHAN Ronghui, ZHAI Qinglin, et al. Multi- view polarization HRRP target recognition based on joint sparsity[J]. Journal of Electronics & Information Technology, 2016, 38(7): 1724-1730. doi: 10.11999/JEIT151019.
[5]
BERIZZI F, MARTORELLA M, CAPRIA A, et al. H/ polarimetric features for man-made target classification[C]. Proceedings of the IEEE Radar Conference, Rome, 2008: 1-6. doi: 10.1109/RADAR.2008.4721003.
YANG Lei, Wang Xiaodan, ZHANG Yuxi, et al. Radar target recognition approach based on multi polarization multi target feature extraction and SVM[J]. Modern Defence Technology, 2012, 40(5): 150-155. doi: 10.3969/j.issn.1009-086x.2012.05. 029.
LEI Lei, WANG Xiaodan, XING Yaqiong, et al. Multi- polarized HRRP classification by SVM and DS evidence theory[J]. Control and Decision, 2013, 28(6): 861-866. doi: 10.13195/j.cd.2013.06.63.leil.011.
GUO Lei. Wideband radar target polarimetric feature extraction and recognition method based on kernel method [D]. [Ph.D. dissertation], National University of Defense Technology, 2009: 15-49.
[9]
LIU H, SUN J, LIU L, et al. Feature selection with dynamic mutual information[J]. Pattern Recognition, 2009, 42(7): 1330-1339. doi: 10.1016/j.patcog.2008.10.028.
[10]
UNLER A, MURAT A, and CHINNAM R B. mr 2 PSO : a maximum relevance minimum redundancy feature selection method based on swarm intelligence for support vector machine classification[J]. Information Sciences, 2011, 181(20): 4625-4641. doi: 10.1016/j.ins.2010.05.037.
[11]
GARCIA M, GOMEZ F, MELIAN B, et al. High-dimensional feature selection via feature grouping: a variable neighborhood search approach[J]. Information Sciences, 2016, 326(C): 102-118. doi: 10.1016/j.ins.2015.07.041.
[12]
BROWN G, POCOCK A, ZHAO M J, et al. Conditional likelihood maximization: a unifying framework for information theoretic feature selection[J]. Journal of Machine Learning Research, 2012, 13(1): 27-66.
[13]
LYSIAK R, KURZYNSKI M, and WOLOSZYNSKI T. Optimal selection of ensemble classifiers using measures of competence and diversity of base classifiers[J]. Neurocomputing, 2014, 126(1): 29-35. doi: 10.1016/j.neucom. 2013.01.052.
[14]
KWAK N and CHOI C H. Input feature selection for classification problems[J]. IEEE Transactions on Neural Networks, 2002, 13(1): 143-159. doi:10.1109/72.977291.
[15]
PENG H, LONG F, and DING C. Feature selection based on mutual information: criteria of max-dependency, max- relevance, and min-redundancy[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2005, 27(8): 1226-1238. doi: 10.1109/TPAMI.2005.159.
[16]
ESTEVEZ P A, TESMER M, PEREZ C A, et al. Normalized mutual information feature selection[J]. IEEE Transactions on Neural Networks, 2009, 20(2): 189-201. doi: 10.1109/TNN. 2008.2005601.
[17]
ISAKOV S V, ZINTCHENKO I N, RONNOW T F, et al. Optimised simulated annealing for icing spin glasses[J]. Computer Physics Communications, 2015, 192: 265-271. doi: 10.1016/j.cpc.2015.02.015.
