GLRT-based Detection Algorithm for Polarimetric MIMO Radar Against SIRV Clutter

This paper mainly deals with target detecting problem using polarimetric Multiple Input Multiple Output (MIMO) radar against Spherically Invariant Random Vector (SIRV) clutter. First, we develop the MIMO signal model to two polarimetric channels and SIRV clutter-dominated scenario, and then the Generalized Likelihood Ratio Test (GLRT) is derived with known covariance structure. Meanwhile, three estimation strategies of covariance, such that Sampled Covariance Matrix (SCM), Normalized Sampled Covariance Matrix (NSCM) and Fixed Point Estimation (FPE) matrix, are introduced to make derived receiver fully adaptive. A thorough performance assessment is given by several numerical examples, and the results show that the polarimetric diversity and the spatial diversity can be exploited to improve the detection performance, and it outperforms the conventional polarimetric phased-array counterpart. Meanwhile, the FPE strategy is more suitable to implement the adaptive detection algorithm, the adaptive loss of which is completely acceptable in practical applications.