Adaptive Bayesian detection using polarimetric MIMO radar in spatially heterogeneous clutter

This paper considers the target detection problem using the distributed polarimetric MIMO (P-MIMO) radar in the presence of spatially heterogeneous clutter. The polarimetric covariance matrices (PCMs) of the primary and the secondary data are assumed to be random with partial priori knowledge of the environment, sharing some appropriate joint distribution. Two-step strategy is employed to design adaptive detector. Specifically, we first obtain the generalized likelihood ratio test (GLRT) detector by assuming the known PCMs. Then, we derive the maximum posteriori (MAP) estimator of the PCMs by exploiting the priori information, and replace the exact PCMs with their MAP estimates. Finally, we evaluate the proposed adaptive detector via numerical simulations.