基于地形融合分类的分区二维恒虚警检测器

在非均匀杂波背景下，由于恒虚警(CFAR)检测器与杂波背景幅度分布不匹配，导致检测器性能出现剧烈下降。针对此问题，提出了一种基于地形融合分类的分区二维CFAR检测器。首先提出一种基于拟合优度(GoF)的地形融合分类算法来对非均匀杂波背景下的地形进行分类编号，同时记录各地形的幅度分布及幅度分布参数；再根据地形编号及记录的幅度分布选择相匹配的CFAR处理窗实现分区二维CFAR；最后利用实测数据验证了该地形融合分类算法的有效性，并用半实测、实测数据对所提出CFAR检测器性能进行了仿真验证，结果表明相比传统二维CFAR检测器，所提出的CFAR检测器在非均匀背景下性能有明显提高。

A partition two-dimensional constant false alarm rate detector based on terrain fusion classification

In heterogeneous clutter background the constant false alarm rate(CFAR)detectors always mismatch the actual clutter background characteristics which lead to a sharp decline in the detector performance.In order to solve the problem,a partition two-dimensional CFAR detector based on terrain fusion classification is proposed. Firstly,a terrain fusion classification algorithm is proposed based on the goodness of fit(GoF) to realize the terrain classification numbering under complex heterogeneous clutter and the amplitude distribution and amplitude distribution parameters recording. And then with the numbered terrain and the recorded amplitude distribution,CFAR processing window selects the amplitude distribution matched CFAR detector to achieve the two-dimensional partitions CFAR. Finally,the measured data proves the effectiveness of the terrain fusion classification algorithm. The half measured data and measured data is provided to demonstrate the performance of the proposed CFAR detector,and the result shows that compared with the traditional two-dimensional CFAR detector,the proposed one has significant improvement in heterogeneous clutter background.