GLRT-based detectors for FDA-MIMO radar with training data

Abstract

In this paper, a frequency diverse array multi-input multi-output (FDA-MIMO) radar is used for moving target detection in unknown Gaussian noise. To solve the detection problem, we adopt the generalized likelihood ratio test (GLRT) and use the training data to estimate the unknown noise covariance matrix. We derive the GLRT both for the case of unknown target velocity and the case of known target velocity. We also derive the analytical expressions for the probabilities of detection (PDs) and the probabilities of false alarm (PFAs), which indicate that the proposed detectors have constant false alarm rate (CFAR) properties. Theoretical and simulation results show that the proposed detectors can provide higher PDs than the existing detectors which do not utilize training data.