A Bayesian CFAR detector for interference control in Weibull clutter

In modern radar system, constant false alarm rate (CFAR) detection is a key technique for automatic target detection in unknown and nonstationary environment. Recently, a novel Bayesian methodology has been introduced for the design of CFAR detector. This method uses Bayesian predictive inference approach to produce a predictive density of the cell under test (CUT) conditioned on the reference samples. The probability of false alarm (Pfa) can then be calculated by integrating this density. As a result, a Bayesian CFAR detector is produced. In this paper, we propose a Bayesian CFAR detector for Weibull clutter under the assumption that the shape parameter is known and extend the Weibull CFAR detector for interference control where the number of interfering targets is determined or not. The simulation results verify the immunity of the proposed detector to the presence of interference.