Radar Target Recognition Based on Kernel Uncorrelated Discriminant Subspace of GSVD
Liu Hua-lin①②; Yang Wan-lin①
①College of Electronic Engineering, UEST of China, Chengdu 610054, China;②Fire Control Technology Center of China South Industry Group Co., Chengdu 611731, China
Abstract: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.