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.
作者
范学满
胡生亮
贺静波
FAN Xueman HU Shengliang HE Jingbo(Institute of Electronics Engineering, Naval University of Engineering, Wuhan 430033, Chin)
出处
《电子与信息学报》
EI
CSCD
北大核心
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)~~
关键词
全极化HRRP
特征提取
特征选择
互信息
模拟退火
Fully polarized HRRP
Feature extraction
Feature selection
Mutual information
Simulated annealing
