Knowledge-aided parametric GLRT for space-time adaptive processing

In this paper, we consider knowledge-aided space-time adaptive processing (KA-STAP) with a parametric approach, where disturbances in both test and training signals are modeled as a multichannel auto-regressive (AR) model. The a priori knowledge is incorporated into the detection problem through a stochastic signal model, where the spatial covariance matrix of the disturbance is assumed random. According to this model, a Bayesian version of the parametric generalized likelihood ratio test (PGLRT) is developed in a two-step approach, which is referred to as the KA-PGLRT. Interestingly, the KA-PGLRT employs a colored loading approach for estimation of the spatial covariance matrix of the test signal. Simulation results show that the KA-PGLRT can obtain better detection performance over other parametric detectors.