基于检测统计量的修正转换恒虚警检测算法

提出了一种新的恒虚警检测算法--修正的转换恒虚警检测算法(MS-CFAR).该算法考虑检测单元采样作为参考单元的选择依据,其使用了基于转换恒虚警(S-CFAR)和最大选择恒虚警(GO-CFAR)的复合算法.给出了该算法在均匀背景中的数学分析.结果表明,该检测器既具有均匀背景下和CA-CFAR相近的良好性能,在多目标环境中亦克服了GO-CFAR不能分辨主目标的弊端,具有较强的适应性.尤其在杂波边缘环境中,MS-CFAR表现出了远好于S-CFAR的控制虚警的能力.

Constant False Alarm Rate Detection Algorithm of Modification Switching based on Test Cell Statistics

In this paper, a new Constant False Alarm Rate (CFAR) algorithm which is a Modification of the Switching CFAR(S-CFAR) that takes into account the statistics of the sample in the test cell for reference sample selection is proposed. It employs a composite approach based on the Switching CFAR(S-CFAR) and Greatest of Selection (GO-CFAR). Mathematical analysis in a homogeneous environment is presented. The simulation results show that the detection performance of MS-CFAR is similar to that of CA-CFAR in homogeneous background and it resolves multiple targets that the GO-CFAR can't resolves, and it shows good robustness. Especially in clutter edge, the detection performance of the Modified S-CFAR is far better than that of S-CFAR.