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This paper considers the problem of knowledge-aided space-time adaptive processing (KA-STAP) combined with a parametric technique. Specifically, by modeling the disturbance as a multichannel autoregressive (AR) process, we introduce a stochastic signal model in which the spatial covariance matrix of the disturbance is assumed to be random, with some prior distribution. Incorporating the a priori knowledge...
The problem of adaptive detection for a multichannel signal in space-time colored compound Gaussian clutter is studied. The speckle component of the clutter is modeled as a multichannel autoregressive (MAR) process. Firstly, assuming that the MAR parameters are known, we derive a parametric generalized likelihood ratio test (PGLRT) against compound Gaussian clutter. By replacing the ideal MAR parameters...
In this paper, we propose a new two-stage Rao test detector, which is obtained by cascading a subspace detector (SD) and the Rao test. A set of secondary data is available and noise returns in primary and secondary data share the same statistical characterization. Analytical expressions for the probability of false alarm (Pfa) and the probability of detection (Pd) are provided in homogeneous environment...
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