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作 者:陈伯孝[1] 陈宇[1] 付芸磊 杨福 CHEN Baixiao;CHEN Yu;FU Yunlei;YANG Fu(National Key Laboratory of Radar Signal Processing,Xidian University,Xi’an 710071,China)
机构地区:[1]西安电子科技大学雷达信号处理全国重点实验室,陕西西安710071
出 处:《安徽工业大学学报(自然科学版)》2023年第3期324-332,共9页Journal of Anhui University of Technology(Natural Science)
基 金:国家自然科学基金项目(61971323)。
摘 要:毫米波道路监控雷达具有全天时、全天候、监控距离远等优点,在交通安全、交通流量、道路监控等方面发挥重要作用,但多径鬼影目标一直影响这种雷达的性能。在分析高速场景下多径传播路径的基础上建立回波信号模型,对恒虚警检测得到目标与多径检测点的特征进行提取,分析多径鬼影与道路护栏的距离和角度、检测点云连续性等特征,据此提出一种基于神经网络的鬼影抑制方法;通过实测数据分析,验证毫米波道路监控雷达目标检测和跟踪的准确性。结果表明:综合利用所提的8种特征可有效抑制多径鬼影目标,多径鬼影识别率达95.74%,总体识别率达90.70%,可实现对车辆的有效监测与定位。Millimeter-wave road monitoring radar has the advantages of all-day,all-weather,and long-distance monitoring,and plays an important role in traffic safety,traffic flow,road monitoring and other fields.However,the multipath ghost target has always affected the performance of this radar.Firstly,the echo signal model of radar was established on the basis of analyzing the propagation paths of multipath signals in highway scenarios.Secondly,the constant false alarm rate detection technique was employed to extract the relevant features of the target in the presence of multipath detection points.Additionally,the distance and angle between multipath ghost targets and road guardrails,the continuity of detection point clouds and other features were analyzed.Finally,a neural network-based ghosting suppression method was proposed based on these identified features.Through empirical data analysis,the accuracy of target detection and tracking by millimeter-wave road monitoring radar was verified.The results indicate that the utilization of the proposed 8 features effectively mitigate the presence of multipath ghosts,with a correct recognition rate of 95.74%and an overall recognition accuracy rate of 90.70%.The method enables effective monitoring and precise positioning of vehicles.
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