Adaptive selection method for generalized likelihood ratio test

Abstract

The generalized likelihood ratio test (GLRT) for spatial diversity detection is widely used in target detection problems in multiple input, multiple output (MIMO) radar systems. GLRT is derived using the probability density function (pdf) of the noise signal in a homogeneous noise environment. In a nonhomogeneous noise environment, however, the received signal is represented as a summation of several signals with different distributions such that it is difficult to estimate the pdf of the received signal. In addition, GLRT requires knowledge of the number of received signals that contain nonhomogeneous noise components, which is not easy to obtain from the received signals. In this paper, we propose an adaptive selection method for GLRT (ASMGLRT) that overcomes the noise nonhomogeneity problem in MIMO radars. The proposed algorithm estimates the noise statistics via the received signals that contain homogeneous noises only, instead of using all received signals in a reference window. Furthermore, it does not require information on the pdfs of the nonhomogeneous noises and the number of received signals containing nonhomogeneous noises when the received signal has both homogeneous and nonhomogeneous noises. The performance of the proposed algorithm is obtained in terms of detection and false alarm probabilities and is compared to GLRT in both homogeneous and nonhomogeneous noise environments.