Bayesian CFAR Detector in Weibull Clutter via Interference Control

Bayesian predictive inference plays an important role in radar constant false alarm rate (CFAR) detection, which produces an adaptive target detection model to achieve CFAR detection. However, in the context of heterogeneous Weibull clutter environments, the interference effect may lead to an imprecise prediction of the background clutter level, resulting in a severely degraded detection performance. To enhance the detection performance in heterogeneous environments, a Bayesian CFAR detector based on interference control is proposed. The Weibull background with known shape parameters is analyzed, based on which the Bayesian interference control detection model is established. Furthermore, in the proposed detector, the interferences in the clutter range profile can be predicted and compensated, which improves the detection performance and anti-interference ability of the detector in heterogeneous environments. Simulation results verify the effectiveness of the proposed detector.