摘要
When tracking a unmanned aerial vehicle(UAV)in complex backgrounds,environmen-tal noise and clutter often obscure it.Traditional radar target tracking algorithms face multiple lim-itations when tracking a UAV,including high vulnerability to target occlusion and shape variations,as well as pronounced false alarms and missed detections in low signal-to-noise ratio(SNR)envi-ronments.To address these issues,this paper proposes a UAV detection and tracking algorithm based on a low-frequency communication network.The accuracy and effectiveness of the algorithm are validated through simulation experiments using field-measured point cloud data.Additionally,the key parameters of the algorithm are optimized through a process of selection and comparison,thereby improving the algorithm's precision.The experimental results show that the improved algo-rithm can significantly enhance the detection and tracking performance of the UAV under high clutter density conditions,effectively reduce the false alarm rate and markedly improve overall tracking performance metrics.
基金
supported in part by National Natural Science Founda-tion of China(No.62372284)
in part by Shanghai Nat-ural Science Foundation(No.24ZR1421800).
作者简介
Hongmei Shi received the B.E.degree from Shanghai University,Shanghai,China,in 2023.She is currently studying toward the master’s degree in School of Communication and Information Engineering,Shanghai University,Shanghai,China.She is interested in integrated communication and sensing technology;Yifan Zhou received the B.S.degree in Electrical Engineering and Automation from East China Jiaotong University,Nanchang,China in 2023.He is currently studying for a master’s degree in School of Communication and Information Engineering,Shanghai University,Shanghai,China.He is interested in integrated communication and sensing;Mengxin Yang received the B.E.degree from Shanghai University,Shanghai,China,in 2024.She is currently working toward the master’s degree with School of Communication and Information Engineering,Shanghai University,Shanghai,China.Her current research interests include integrated sensing,communication and computation network,and satellite communication;Corresponding author:Dan Zeng received her Ph.D.degree in circuits and systems,and her B.S.degree in electronic science and technology,both from University of Science and Technology of China,Hefei.She is a full professor and the Dean of School of Communication and Information Engineering at Shanghai University,directing Computer Vision and Pattern Recognition Lab.Her main research interests include computer vision,multimedia analysis,and machine learning.She is serving as the Associate Editor of the IEEE Transactions on Multimedia and the TC Member of IEEE MSA and IEEE MMSP,Email:dzeng@shu.edu.cn。
