摘要
无人机作为典型的“低小慢”目标,具有飞行速度慢、高度低、雷达反射面积小等特点,使得无人机目标很难被检测和识别。针对无人机在复杂环境下存在信噪比低、检测困难等问题,提出了一种知识辅助的无人机目标恒虚警率(CFAR)检测方法。首先分析3种常见的地杂波分布模型和均值类CFAR检测器,然后分别对这3种杂波分布下的回波信号采取CFAR检测方法,将检测性能最优的方法作为该杂波分布下最优的CFAR检测方法存入知识库,从而建立CFAR知识库;通过对需要检测目标的回波信号的杂波分布进行估计,判断杂波分布的模型,并以此分布从雷达知识库中选择所对应的CFAR算法,从而完成回波信号的检测。利用雷达采集的实测数据进行了验证,仿真和实验结果验证了该方法的可行性和有效性。
As a typical "low,small,slow" target,UAV has the characteristics of slow flight speed,low altitude,and small radar reflection area(RCS),making it difficult to detect and identify UAV targets.In view of the problem of low signal-tonoise ratio and difficult detection of UAVs in complex environments,a knowledge-aided constant false alarm rate(CFAR)detection method for UAV targets is proposed.This method first analyzes three common ground clutter distribution models and mean CFAR detectors,and then adopts CFAR detection methods for the echo signals under the three clutter distributions,and uses the method with the best detection performance as the clutter distribution The optimal CFAR detection method is stored in the knowledge base to establish the CFAR knowledge base;by estimating the clutter distribution of the echo signal of the target to be detected,the clutter distribution model is judged,and the distribution is obtained from the radar knowledge base Select the corresponding CFAR algorithm to complete the echo signal detection.Finally,the actual measurement data collected by radar is used to verify the feasibility and effectiveness of the method.
作者
王峰
晋良念
WANG Feng;JIN Liangnian(School of Information and Communication,Guilin University of Electronic Technology,Guilin 541004,China;Guangxi Key Lab of Wireless Wide Band Communication and Signal Processing,Guilin University of Electronic Technology,Guilin 541004,China)
出处
《桂林电子科技大学学报》
2022年第3期211-216,共6页
Journal of Guilin University of Electronic Technology
基金
广西科技重大专项(桂科AA17202048-2)
桂林电子科技大学研究生教育创新计划(2020YCXS023)。
关键词
无人机
目标检测
知识辅助
恒虚警率
杂波分布模型
UAV
target detection
knowledge aided
constant false alarm rate
clutter distribution model
