Adaptive Bayesian Detection Using MIMO Radar in Spatially Heterogeneous Clutter

This letter considers adaptive target detection problem using multiple-input multiple-output (MIMO) radar in the presence of spatially heterogeneous clutter. The covariances of the primary data and secondary data for the same and different transmit-receive pairs are modeled as different random matrices with partial priori knowledge of the environment. Two-step strategy is employed to design adaptive detector. Specifically, we first obtain the generalized likelihood ratio test (GLRT) detector by assuming the known matrices. Then, we derive the maximum posteriori (MAP) estimator of the covariance matrices by exploiting the priori information, and replace the given covariance matrices in the obtained GLRT with MAP estimates. Finally, we evaluate the proposed adaptive detector via numerical simulations.