对海雷达目标识别中全极化HRRP的特征提取与选择  

Feature Extraction and Selection of Full Polarization HRRP in Target Recognition Process of Maritime Surveillance Radar

作　　者：范学满 胡生亮[1] 贺静波[1] FAN Xueman HU Shengliang HE Jingbo(Institute of Electronics Engineering, Naval University of Engineering, Wuhan 430033, Chin)

机构地区：[1]海军工程大学电子工程学院,武汉430033

出　　处：《电子与信息学报》2016年第12期3261-3268,共8页Journal of Electronics & Information Technology

基　　金：国家自然科学基金(61401493);国家部委基金(9140A01010415JB11002)~~

摘　　要：充分、有效地利用目标全极化HRRP的特征信息是提高对海雷达目标识别率的研究热点之一。该文利用CST软件仿真建立了7类海上目标在不同方位角下的全极化HRRP数据库。在此基础上,提取了4类共39个特征。提出一种基于归一化互信息(NMI)并利用模拟退火(SA)算法进行优化的全局最优特征选择算法,并命名为NMI-SA。基于HRRP数据集以及9个UCI数据集,利用k-近邻分类器将该算法与另外3种常用的特征选择算法进行对比,结果表明新算法选择的特征具有良好的可分性和较低的冗余度,最终用于分类时的正确率总体优于其余3种算法。最后,用该算法对全极化HRRP的39个特征进行重点分析,选择出25个辨别力强、冗余度低的特征。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.

关 键 词：全极化HRRP 特征提取 特征选择 互信息 模拟退火 

分 类 号：TN959.72[电子电信—信号与信息处理]