A Robust Weighted Likelihood Constant False Alarm Rate detector algorithm based on Bayesian interference control，BRWL-CFAR，is introduced，which dynamically evaluates the clutter level in Weibull background by equally dividing the clutter range profile and optimizing the decision selection. At the same time，the clutter level carries out feedback control on the former. Then，in order to realize multi-target predictive inference in Weibull background，based on Bayesian interference control theory，the idea of using constant false alarm detector in different scenarios by Bayesian classified interference control is proposed. Therefore，the anti-interference ability of the detector is improved while reducing the computational complexity. The false alarm rate and judgment expression are given，and the detection is extended when the segmentation and interference are arbitrary. The SAR image data obtained by TerraSAR-X satellite is used for simulation experiments. The results show that the proposed detector is more robust than the traditional detector algorithms of CA-CFAR，OS-CFAR，TM-CFAR and WAI-CFAR.