基于多背景杂波分布模型的自适应CFAR检测

The Adaptive CFAR Detection Algorithm Based on the Multiple Background Clutter Distribution Model

针对现有的CFAR(Constant False Alarm Rate,恒虚警)算法采用全局建模,在待检测的所有区域采用同种背景杂波分布模型,导致使用的模型在不适应区域失配,使CFAR检测性能下降的现象,本文提出一种基于多背景杂波分布模型的自适应CFAR检测算法。该方法根据背景区域的不同统计特性即统计方差和均值比来判断区域类型,采用CFAR检测器自适应地根据区域类型选择相应的背景杂波分布模型,即在均匀区域采用高斯分布;在有杂波边缘的区域,采用韦布尔分布以消除杂波边缘的影响;在有多目标干扰的区域采用G0分布以排除干扰目标,避免相邻目标的相互屏蔽效应。实验结果表明,此方法使检测结果得到明显提高。

Global modeling is adopted in existing Constant False Alarm Rate （CFAR） algorithms, and the same distribution model is used which estimates the background clutter to detect the whole area. But practical ground covers complex types, and different ground area has its most suitable backgrounds model. The used model is not fit in some regional, making higher loss of CFAR, bringing down the test performance. So an algorithm is presented which judged the areas according to the different characteristics of background, such as statistical variance and mean ratio. In this way. CFAR detector could select the distribution model on the basis of the regional type automatically and get the best detection results： that is, choosing Gaussian distribution in an uniform region. Weibull distribution is used to eliminate the influence while in a clutter edge, and Go distribution is used to eliminate the obstacles targets while in a multiple targets interfering region, avoiding mutual shielding effects of adjacent targets.