Bayesian Optimum Radar Detector in non-Gaussian noise

In this paper, a theoretical expression of the optimum non-Gaussian radar detector is derived from the non-Gaussian SIRP model (Spherically Invariant Random Process) clutter and a bayesian estimator of the characteristic function of the SIRP. The SIRP model is used to perform coherent detection and to modelize the clutter as a complex Gaussian process whose variance is itself a positive random variable (r.v.). The PDF of the variance characterizes the statistics of the SIRP and after performing a bayesian estimation of this PDF from reference clutter cells we derive the Bayesian Optimum Radar Detector (BORD) and its statistical asymptotic form without any knowledge about the statistics of the clutter. We evaluate BORD performance for an unknown target signal embedded in K-distributed clutter and compare with optimum detectors performance (such as Optimum K Detector - OKD - in K-distributed clutter).