Knowledge-aided detection for airborne MIMO radar by exploiting structured clutter spectrum

In this study, the authors propose two knowledge-aided detection schemes based on prior structured clutter information for airborne collocated multiple-input multiple-output (MIMO) radars. Based on a first-order representation of the clutter spectrum, they propose the first-order generalised likelihood ratio test (FO-GLRT) detector. Meanwhile, Based on a second-order representation of the clutter spectrum, they propose the second-order GLRT (SO-GLRT) detector. They also analyse and compare the detection performance of those two detectors in different situations. The FO-GLRT detector can achieve acceptable performance without any secondary data, and thus immune to the clutter heterogeneity. The SO-GLRT detector that exploits the structured clutter covariance matrix is able to achieve a nearly optimal performance in certain cases. Both the proposed detectors are suitable for the collocated MIMO radars, which have a high signal dimension and a large demand for independent identically distributed training samples. Simulations validate those results.