Tunable Adaptive Target Detection With Kernels in Colocated MIMO Radar

Abstract

We introduce a framework for exploring adaptive target detection in colocated MIMO radar in non-linear feature space by exploiting the theory of kernel. The kernel theory inspires us to replace the inner products of test statistics with that of nonlinear mapped data in the feature space or that of their kernel tricks to achieve better detection performance. We apply this framework to the problem of tunable adaptive target detection in colocated MIMO radar, according to the principle of the generalized likelihood ratio (GLR), Rao and Wald (RaW) tests, and propose several new detectors under two new tunable detector forms, namely kernel tunable GLR-based and kernel tunable Raw-based detectors. The proposed tunable detectors include most of the prior detectors in colocated MIMO radar as special cases. One capability of the proposed detectors is that their robustness or selectivity (RoS) to steering vector mismatch (SVM) can be tuned flexibility through an RoS tuning parameter. In addition, we are able to incorporate the prior distribution (PD) of the disturbance covariance matrix, if available, through a PD tuning parameter. Therefore, the proposed detectors can be tuned based on the tuning RoS parameter to achieve robust or selective performance in the presence of SVM as well as to switch between the Bayesian or non-Bayesian based detectors through the PD parameter. For practical situations, we show that the proposed detectors possess CFAR property against disturbance covariance matrix by resorting to the invariance principle. Extensive Monte Carlo simulation results are provided to indicate that the proposed detectors have better detection performance than their counterparts in both single-target and multi-target scenarios.