Knowledge-Aided Range-Spread Target Detection for Distributed MIMO Radar in Nonhomogeneous Environments

Abstract

This paper deals with the problem of detecting a moving range-spread target in distributed MIMO radar. A new knowledge-aided (KA) model that takes into account the nonhomogenous characteristics of the disturbance (clutter and noise) in distributed MIMO radar is proposed. Specifically, the disturbance covariance matrices corresponding to different transmit-receive (Tx-Rx) pairs are modeled as random matrices. These covariance matrices share a prior covariance matrix structure but with different power levels to model the nonhomogeneous clutter powers across different Tx-Rx pairs. Two cases are considered, involving either no range training (i.e., when the disturbance is highly nonhomogeneous) or some range training data. For the first case, we develop a KA generalized likelihood ratio test (GLRT) for range-spread target detection, along with a simplified version of the KA-GLRT for point-like target detection. For the second case, the KA-GLRT becomes computationally intractable, a simple ad-doc KA detector is introduced to take advantage of training data for range-spread target detection. Simulation results are presented to illustrate the performance and effectiveness of the proposed detectors in nonhomogeneous environments.