Parametric Adaptive Target Detection in Colocated MIMO Radar

This article considers the problem of target detection in colocated multiple-input multiple-output radar under the Gaussian disturbance with unknown covariance when the sample number of transmitted waveform is small. We model the disturbance as an autoregressive (AR) process with unknown parameters. Three parametric adaptive detectors are designed according to the generalized likelihood ratio test (GLRT), Rao test, and Wald test. Notably, the training data are not required in the proposed detectors. We point out that the detection problem to be solved can be equivalent to a hypothesis test within the context of subspace interference. From this perspective, these detectors exhibit different echo projections to achieve the accumulation of useful signals. Moreover, the asymptotic detection performance is analyzed when the AR process matches or mismatches with the actual disturbance. Numerical simulations show that the proposed detectors outperform their competitors. The proposed Wald detector has stronger robustness against the mismatched signals, and the proposed Rao detector is more sensitive to the mismatch in the steering vector. The proposed GLRT detector exhibits better false alarm control ability.