Adaptive detection with training data in partially homogeneous environments for colocated MIMO radar

Abstract

The issue of adaptive detection is considered for the colocated multi-input multi-output (MIMO) radar in this paper. Meanwhile, the background is partially homogeneous environments (PHE), where the power mismatch is present between the training data and test data. The training data in PHE are utilized to derive effective detectors on the basis of generalized likelihood ratio test, Rao, Wald, Gradient, and Durbin tests. Since the Durbin test-based detector coincides with the Rao test-based detector, the two-step design approach for the Durbin test is used for deriving the new detector. The outcomes of simulation experiments illustrate that the proposed detectors achieve superior effectiveness to existing approaches. Furthermore, the results also show that when the signal mismatch exists, the Wald and Durbin tests maintain robust characteristics. Meanwhile, the Rao test ensures selective property.