Enhanced Kernel Uncorrelated Discriminant Nearest Feature Line Analysis for Radar Target Recognition
Chunyu Wan, Xuelian Yu, Yun Zhou, Xuegang Wang · 2014
In this paper, a new subspace learning algorithm, called enhanced kernel uncorrelated discriminant nearest feature line analysis i¼EKUDNFLAi¼, is presented. The aim of EKUDNFLA is to seek a feature subspace in which the within-class feature line (FL) distances are minimized and the between-class FL distances are maximized simultaneously. At the same time, an uncorrelated constraint is imposed to get statistically uncorrelated features, which contain minimum redundancy and ensure independence, and thus it is highly desirable in many practical applications. Optimizing an objective function in a kernel feature space, nonlinear features are extracted. In addition, a weighting coefficient is introduced to adjust the proportion between within-class and between-class information to get an optimal effect. Experimental results on radar target recognition with measured data demonstrate the effectiveness of the proposed method.