基于线性卷积系数扩展特征的雷达目标识别  

Radar target recognition based on the extended feature of linear convolution coefficients

作　　者：朱劼昊[1] 周建江[1] 吴杰[1] 

机构地区：[1]南京航空航天大学信息科学与技术学院,江苏南京210016

出　　处：《系统工程与电子技术》2010年第1期45-48,共4页Systems Engineering and Electronics

基　　金：装备预研重点基金(N0601041);中国电子科技集团第14研究所院士基金(2008041001)资助课题

摘　　要：针对雷达目标高分辨距离像识别中的有效特征提取问题,提出了一种基于线性卷积系数扩展特征的雷达目标识别方法。该方法将高分辨距离像及其线性卷积系数扩展特征作为联合特征在核空间中进行特征选择,并采用支持向量机(support vector machine,SVM)作为分类器实现雷达目标识别。核空间中的特征选择可以解决联合特征高特征维数问题和非线性可分问题,进而提高SVM识别性能,而线性卷积系数扩展特征相比高分辨距离像具有更强的稳定性。同时,可以在一定程度上弥补因特征选择带来的高分辨距离像部分距离单元特征分量缺失。基于5种飞机目标高分辨距离像的仿真实验证明了该方法的有效性。To extract the effective features of the high-resolution range profile （HRRP）, a radar target recognition approach based on the extended feature of linear convolution coefficients is proposed. By this approach, the joint features which are composed of HRRP and its linear convolution coefficients are selected in the kernel space, and then the support vector machine （SVM） is applied to classification. Feature selection in the kernel space has the ability to solve the high dimensionality problems and nonlinear separability problems of joint features, thus improving the SVM recognition performances. On the other hand, the extended feature of linear convolution coefficients is more stable compared with HRRP. Moreover, this extended feature can make up for the losses of partial range cells caused by feature selection. Simulations results based on the HRRP dataset of five aircraft models demonstrate the validity of the proposed approach.

关 键 词：雷达自动目标识别 高分辨距离像 特征提取 特征选择 支持向量机 

分 类 号：TN911.7[电子电信—通信与信息系统]