基于有序统计和最大参考单元选择的CFAR算法

CFAR Algorithm Based on Ordered Statistics and Maximum Reference Cell Selection

针对OS-CFAR在杂波边缘环境下虚警率偏高的问题,提出了一种基于有序统计和最大参考单元选择的CFAR算法。首先将参考窗分成若干个子参考窗。然后在每个子参考窗内,采用传统OS-CFAR的策略选择参考单元。最后从被选择的参考单元中,选择背景噪声功率最大的参考单元,并乘以门限因子得到检测门限。仿真实验表明,在均匀环境和多目标环境下,改进的CFAR的检测性能略低于OS-CFAR。但是在杂波边缘环境下,尤其是在虚警峰值时,改进的CFAR的性能明显优于OS-CFAR。

In view of the problem of high false alarm rate of OS-CFAR in clutter edge environment,proposes a CFAR algorithm based on ordered statistics and maximum reference cell selection.Firstly,the reference window is divided into several sub-reference windows.Then,the traditional OS-CFAR strategy is adopted to select reference cells in each sub-reference window.Finally,the reference cell with the highest background noise power is selected from the selected reference cells and multiplied by the threshold factor to obtain the detection threshold.Simulation results show that the detection performance of the improved CFAR is slightly lower than that of OS-CFAR in homogeneous environment and multi-target environment.However,in clutter edge environment,especially at false alarm peak,the performance of the improved CFAR is significantly better than that of OS-CF AR.