多目标环境中的自适应学习恒虚警率检测算法

A Adaptive Learning Constant False Alarm Rate Detection Algorithm in Multiple-target Interference Process

针对多目标干扰过程中参考单元内目标干扰检测单元检测,提出一种自适应学习恒虚警检测算法。该算法根据选定参考的历史样本依次加权递推出估计杂波功率水平。仿真结果表明,加权递推滤波在剔除干扰和在杂波边缘中检测目标方面表现出了良好的性能,在对抗杂波边缘WRF-CFAR更是可取的。在均匀杂波背景中改进的CWRF-CFAR总体性能优于WRF-CFAR且随滤波长度的增加优势扩大。

For multi-target interference reference unit in the process of target detection unit, a adaptive learning CFAR algorithm is presented. The algorithm is based on the history of the selected reference samples respectively weighted recursive estimate clutter power level. Simulation results show that weighted recursive filter in eliminating interference and target detection in clutter edge showed good performance,WRF -CFAR is more desirable in the fight against clutter edge. The overall performance by the improved CWRF-CFAR is better than WRF-CFAR and with the increase of the length of filtering the lead in the homogeneous clutter background.