对海雷达目标识别中全极化HRRP的特征提取与选择

范学满; 胡生亮; 贺静波

doi:10.11999/JEIT160722

对海雷达目标识别中全极化HRRP的特征提取与选择

doi: 10.11999/JEIT160722

基金项目:

国家自然科学基金(61401493)，国家部委基金(9140A01010415JB11002)

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- 被引次数: 0
出版历程
- 收稿日期: 2016-07-07
- 修回日期: 2016-11-01
- 刊出日期: 2016-12-19

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

Funds:

The National Natural Science Foundation of China (61401493), The National Ministries Foundation of China (9140A01010415JB11002)

摘要

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。
- 全极化HRRP /
- 特征提取 /
- 特征选择 /
- 互信息 /
- 模拟退火
Abstract: 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.
- Fully polarized HRRP /
- Feature extraction /
- Feature selection /
- Mutual information /
- Simulated annealing

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参考文献(23)

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