Intelligent recognition and information extraction of radar complex jamming based on time-frequency features  

作　　者：PENG Ruihui WU Xingrui WANG Guohong SUN Dianxing YANG Zhong LI Hongwen 

机构地区：[1]College of Information and Communication Engineering,Harbin Engineering University,Harbin 150001,China [2]Qingdao Innovation and Development Center,Harbin Engineering University,Qingdao 266000,China [3]Information Fusion Research Institute,Naval Aeronautical University,Yantai 264001,China [4]National Key Laboratory of Science and Technology on Vessel Integrated Power System,Naval University of Engineering,Wuhan 430033,China

出　　处：《Journal of Systems Engineering and Electronics》2024年第5期1148-1166,共19页系统工程与电子技术（英文版）

基　　金：supported by Shandong Provincial Natural Science Foundation(ZR2020MF015);Aerospace Technology Group Stability Support Project(ZY0110020009).

摘　　要：In modern war,radar countermeasure is becoming increasingly fierce,and the enemy jamming time and pattern are changing more randomly.It is challenging for the radar to efficiently identify jamming and obtain precise parameter information,particularly in low signal-to-noise ratio(SNR)situations.In this paper,an approach to intelligent recognition and complex jamming parameter estimate based on joint time-frequency distribution features is proposed to address this challenging issue.Firstly,a joint algorithm based on YOLOv5 convolutional neural networks(CNNs)is proposed,which is used to achieve the jamming signal classification and preliminary parameter estimation.Furthermore,an accurate jamming key parameters estimation algorithm is constructed by comprehensively utilizing chi-square statistical test,feature region search,position regression,spectrum interpolation,etc.,which realizes the accurate estimation of jamming carrier frequency,relative delay,Doppler frequency shift,and other parameters.Finally,the approach has improved performance for complex jamming recognition and parameter estimation under low SNR,and the recognition rate can reach 98%under−15 dB SNR,according to simulation and real data verification results.

关 键 词：complex jamming recognition time frequency feature convolutional neural network(CNN) parameter estimation 

分 类 号：TN974[电子电信—信号与信息处理]