Adaptive detection of polarimetric MIMO radar in compound-Gaussian clutter

The polarimetric signal model of multiple-input multiple-output (MIMO) radar is formulated, based on the assumption of inverse-Gamma distribution for the texture component of compound-Gaussian clutter. An adaptive polarimetric detector of MIMO radar based on maximum a posteriori estimation and the gene-ralized likelihood ratio test (MAP-GLRT) is proposed. Firstly, the training data are exploited to estimate the covariance matrix of the clutter in the detector to realise the adaptability. Furthermore, the analytic expression of the false alarm probability is derived to prove constant with respect to the clutter energy. Simulation results show that the shape parameter of the clutter and the number of radar channels have an impact on the MAP-GLRT, and the performance of MAP-GLRT is better than its counterparts.