Adaptive Detectors for Colocated MIMO Radar With Training Data

In this letter, we consider the problem of target detection in unknown Gaussian noise for a colocated multi-input multi-output (MIMO) radar. To improve the detection performance, we adopt the training data, which were not utilized in existing references for the considered problem. We derive the generalized likelihood ratio test (GLRT) and Rao and Wald tests. Moreover, the corresponding analytical expressions for the probabilities of detection (PDs) and probabilities of false alarm (PFAs), which indicate that the proposed detectors have constant false alarm rate (CFAR) properties. Simulation results show that the proposed detectors can provide higher PDs than the existing detectors which do not utilize training data.