摘要： 传统的恒虚警率(CFAR)检测器鲁棒性较差，为此，提出一种基于AD检验的CFAR检测器AD-CA。通过Monte Carlo仿真得到AD检验的临界值，并删除异常样本。仿真结果表明，在均匀背景下，AD-CA的检测性能与OS检测器相当；在多目标背景下，AD-CA的检测性能比OS检测器有所提升，当干扰目标个数大于N–k时，仍能保持较好的检测性能。

关键词: 雷达, 目标检测, 多目标背景, 恒虚警率, AD检验

Abstract: For solving the robust disadvantage of the existing Constant False Alarm Rate(CFAR) detectors, a new CFAR detector based on Anderson-Darling(AD) test called AD-CA is proposed. It takes the critical value of AD test through Monte Carlo simulation, then deletes the unwanted samples. It is evaluated through simulation in various environments. Simulation result shows that, in homogeneous background, the performance of AD-CA is as good as OS; In multiple targets background, compared with the OS, the detection performance of AD-CA improves obviously. Especially when the value of interfering targets exceeds N–k, it also keeps good performance.

Key words: radar, target detection, multiple targets background, Constant False Alarm Rate(CFAR), Anderson-Darling(AD) test

中图分类号: 

• TN957.51