Radar target recognition based on kernel projection vector space using high-resolution range profile

In this paper, a novel approach, namely kernel projection vector space (KPVS), is proposed for radar target recognition using high-resolution range profile (HRRP). First, the HRRP samples are mapped into a high-dimensional feature space using nonlinear mapping. Second, the kernel projection vectors, are obtained by kernel discriminant analysis. Then, for each class, the kernel projection vector space is formed using all the training kernel projection vectors of class. Finally, the minimum hyperplane distance classifier (MHDC) is used for classification. The aim of KPVS method is to represent the feature area of target using kernel projection vector space, and effectively measure the distance between the test HRRP and feature area via minimum hyperplane distance (MHD) metric. The experimental results of measured data show that the proposed method has better performance of recognition than KPCA and KFDA.