Supervised kernel discriminant local tangent space alignment for high-resolution range profile-based radar target recognition

Abstract

We present a modified local tangent space alignment (LTSA) algorithm, called supervised kernel discriminant local tangent space alignment (SKDLTSA), for radar target recognition based on high-resolution range profile (HRRP). SKDLTSA aims to extract intraclass geometric structure embedded in local neighborhoods, as well as to maximize interclass separability characterized by overall distances among different classes. It is formulated with kernel technique to extract nonlinear features, which helps to obtain better performance than its linear counterparts. Extensive experiments on measured HRRP data from three flying airplanes demonstrate that the proposed method can significantly improve the recognition performance. Further results also indicate its robustness to target attitude variations and noise corruption.