Knowledge-aided Bayesian MIMO radar detector in heterogeneous clutter

This work addresses the problem of adaptive multiple-input multiple-output (MIMO) radar detector in heterogeneous clutter. We first derive the generalized likelihood ratio test (GLRT) based on the two-step design procedure. Then, considering with the Bayesian framework and the prior knowledge about the clutter, we adopt the Maximum A Posteriori (MAP) estimator to the clutter covariance matrix and extend the knowledge-aided Bayesian technique to MIMO radar detection. Finally, various simulation results and comparison with respect to other conventional technique are presented to demonstrate the effectiveness of the knowledge-aided Bayesian technique, especially in presence of a small amount of secondary data.