Abstract:In this paper, we consider knowledge-aided spacetime 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 i… Show more
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