MIMO radar adaptive Bayesian detection in compound-Gaussian clutter with inverse Gamma texture

This paper addresses the problem of adaptive multiple-input multiple-output (MIMO) radar detection in heterogeneous environment with compound-Gaussian clutter. The clutter covariances are assumed to be random and different from one transmit/receive pair to another with a priori knowledge about the environment. A two-step strategy is employed to design adaptive detector. Firstly, we obtain the generalized likelihood ratio test (GLRT) detector by assuming the known covariance matrices. Then, we derive the maximum a posteriori (MAP) estimator of the matrices by exploiting the Bayesian technique, and replace the given covariance matrices in the obtained GLRT detector with MAP estimates. Finally, we evaluate the proposed adaptive Bayesian detector via numerical simulations.