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.

在现代雷达系统中，恒定的误报率（CFAR）检测是在未知和不稳定的环境中自动进行目标检测的关键技术。最近，一种新颖的贝叶斯方法已经被引入到CFAR检测器的设计中。此方法使用贝叶斯预测推断方法来生成以参考样本为条件的被测细胞（CUT）的预测密度。然后可以通过对该密度进行积分来计算误报的概率（Pfa）。结果，产生了贝叶斯CFAR检测器。在本文中，我们在假设形状参数已知的前提下，提出了一种用于威布尔杂波的贝叶斯CFAR检测器，并扩展了用于确定干扰目标数量的干扰控制的威布尔CFAR检测器。