基于GSVD的核不相关辨别子空间与雷达目标识别  

Radar Target Recognition Based on Kernel Uncorrelated Discriminant Subspace of GSVD

作　　者：刘华林[1,2] 杨万麟[1] 

机构地区：[1]电子科技大学电子工程学院,成都610054 [2]中国兵器装备集团火控技术中心,成都611731

出　　处：《电子与信息学报》2009年第5期1095-1098,共4页Journal of Electronics & Information Technology

基　　金：国家自然科学基金(60372022);新世纪优秀人才支持计划(NCET-05-0806)资助课题

摘　　要：该文提出了一种基于广义奇异值分解的核不相关辨别子空间算法,并将其用于高分辨距离像雷达目标识别。新算法结合广义奇异值分解与核方法的优点,有效地解决了传统方法面临的矩阵奇异问题,同时进一步改善了目标的类可分性。其次,依据Fisher准则导出了距离像总散度矩阵零空间中不含有有用辨别信息的结论。利用这一结论,可以在求解核不相关最优辨别矢量之前对各散度矩阵进行预降维,以减小后续运算的计算复杂度。对3类飞机目标实测数据的识别结果表明了所提方法的有效性。A Kernel Uncorrelated Discriminant Subspace （KUDS） method based on Generalized Singular Value Decomposition （GSVD） for radar target recognition is proposed. The new method combines with the advantage of GSVD and kernel trick, which can effectively overcome the limitation of traditional linear methods in solving singular problem, but also improve the class separability further. In addition, a conclusion from Fisher＇s criterion that there exists no useful discriminative information in the null space of the range profile population scatter matrix is derived, which can be used to reduce the dimensionality of original scatter matrices as well as the computation complexity of the following operation of solving kernel optimal discriminant vectors. Experimental results based on three measured airplanes data confirm the effectiveness of the proposed method.

关 键 词：雷达目标识别 高分辨距离像 核不相关辨别子空间 广义奇异值分解 

分 类 号：TN957.54[电子电信—信号与信息处理]