Kernel Function Based Mean Matrix Estimation and Its Application to Radar Target Detection

Abstract

The estimation of mean matrix for a set of Hermitian positive definite (HPD) matrices is of fundamental importance to radar target CFAR detection, which can be considered based on a geometric framework. However, false targets can cause large errors in the estimation of the mean clutter matrix. In this paper, we consider the nonlinearity of kernel function and the mean matrix estimation method based on kernel function is designed. In order to reduce the influence of false targets in the estimation of mean matrix, we apply the kernel function to the nonlinear adjustment of the geometric distance. Then, the kernel function based constant false alarm rate (CFAR) detector is designed. Finally, numerical experiments based on simulated clutter data and measured sea clutter data are performed. We come to a conclusion that the method of matrix mean value estimation based on kernel function can restrain the false target in the reference cells effectively.