联合Cameron分解和融合RKELM的全极化HRRP目标识别方法  

HRRP Target Recognition Method for Full Polarimetric Radars by Combining Cameron Decomposition and Fusing RKELM

作　　者：王晶晶 刘峥[1] 谢荣[1] 冉磊 WANG Jingjing;LIU Zheng;XIE Rong;RAN Lei(National Laboratory of Radar Signal Processing,Xidian University,Xi’an 710071,China)

机构地区：[1]西安电子科技大学雷达信号处理国家重点实验室,西安710071

出　　处：《雷达学报（中英文）》2021年第6期944-955,共12页Journal of Radars

基　　金：国家自然科学基金(62001346);中国博士后基金面上项目(2019M663632)。

摘　　要：该文针对传统全极化高分辨一维距离像(HRRP)雷达目标识别问题,提出了结合Cameron分解和融合简化核极限学习机(RKELM)的目标识别方法,旨在提高全极化HRRP目标识别性能。在特征提取阶段,所提方法利用Cameron分解定义了目标在各个标准散射体上的投影分量。通过分析,将目标在三面角、二面角和1/4波长器件这3个散射基上沿距离维的投影分量作为目标特征,实现对目标散射特性更加精细化的描述。在分类阶段,考虑到RKELM算法识别性能的不稳定性,提出了一种基于原型聚类预处理的RKELM方法,并在此基础上设计了特征级融合RKELM网络和决策级融合RKELM网络,以对投影特征进行融合分类。实验部分利用10类民用车辆的全极化HRRP数据将所提识别方法和现有方法进行了对比,结果表明:(1)所采用的Cameron分解投影特征表现出了较高的可分性和噪声稳健性;(2)当训练样本数较多时,特征级融合RKELM算法的泛化性能较好;当训练样本数较少时,决策级融合RKELM的泛化性能较好。A recognition method combining Cameron decomposition and fusing Reduced Kernel Extreme Learning Machine(RKELM) is proposed for the Full Polarimetric(FP) High Resolution Range Profile(HRRP)-based radar target recognition task. In the feature extraction phase, Cameron decomposition is exploited to define the projection component of the target on the standard scatterers. Through analysis, the projection components on three scattering bases, i.e., trihedral, dihedral, and 1/4 wave device, are selected as target features, which achieve more detailed descriptions of the target characteristics. In the classification phase, considering the instability of the recognition performance of the RKELM algorithm, the RKELM based on prototype clustering preprocessing is first proposed. Then, to improve the recognition performance, we proposed the feature level fusing RKELM and the decision level fusing RKELM to fuse the three projection components of the targets. The experiments compared the performance of the proposed recognition method and the state-of-the-art methods using the FP HRRP data from 10 civilian vehicles. The results demonstrate that the projection features by Cameron decomposition exhibit higher separability and better noise robustness, and that the feature level fusing RKELM has better generalization performance with a large number of training samples, but the decision level fusing RKELM was better with a small number of training samples.

关 键 词：雷达目标识别 高分辨一维距离像 全极化 Cameron分解 简化核极限学习机 信息融合 

分 类 号：TN95[电子电信—信号与信息处理]