火力与指挥控制2017，Vol.42Issue(1)：49-53,5.
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
摘要
Abstract
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.关键词
多目标干扰/自适应学习/恒虚警检测/加权递推滤波/杂波边缘Key words
multi-target interference/adaptive learning/constant false alarm rate detection/weighed recursive filter/clutter edge分类
信息技术与安全科学引用本文复制引用
罗朝义,程嗣怡,王玉冰..多目标环境中的自适应学习恒虚警率检测算法[J].火力与指挥控制,2017,42(1):49-53,5.基金项目
陕西省自然科学基金资助项目(2012Q8019) （2012Q8019）
