Angular-diversity target recognition by kernel scatter-difference based discriminant analysis on RCS

In this paper, radar target recognition is given by KSDA (kernel scatter-difference discriminant analysis) pattern recognition on RCS (radar cross section). The kernel method converts the traditional FLDA (Fisher linear discriminant analysis) to a nonlinear high-dimensional space and such a kernel technique is called KFDA (kernel Fisher discriminant analysis). The basic concept of KFDA is to map training samples in the original space to a high-dimensional feature space via a nonlinear mapping function. Pattern recognition is then implemented in the feature space through extracted nonlinear discriminant features. However, as the kernel within-class scatter matrix is singular, the optimal discriminant features can not be achieved directly. To improve this drawback of KFDA, this study utilizes the scatter difference as the discriminant function, i.e., KSDA, to implement radar target recognition. The KSDA can modify the Fisher discrimination function and then serves as an efficient tool of radar target recognition. As a result, the computational complexity is reduced and then the computational speed is increased. Of great importance, the proposed target recognition scheme (based on KSDA) can still work well even though the kernel within-class scatter matrix is singular. Our KDSA based target recognition scheme is accurate, efficient and has good ability to tolerate random noises.