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作 者:姬丽彬 朱岩 崔天舒 王栋 黄永辉 JI Libin;ZHU Yan;CUI Tianshu;WANG Dong;HUANG Yonghui(Key Laboratory of Electronic and Information Technology for Complex Space Systems,National Space Science Center,Chinese Academy of Science,Beijing100190,China;School of Computer Science and Technology,University of Chinese Academy of Science,Beijing 100049,China;Beijing National Research Center for Information Science and Technology,Tsinghua University,Beijing 100084,China)
机构地区:[1]中国科学院国家空间科学中心复杂航天系统电子信息技术重点实验室,北京100190 [2]中国科学院大学计算机科学与技术学院,北京100049 [3]清华大学北京信息科学与技术国家研究中心,北京100084
出 处:《北京航空航天大学学报》2025年第4期1324-1331,共8页Journal of Beijing University of Aeronautics and Astronautics
基 金:中国科学院复杂航天系统电子信息技术重点实验室自主部署基金(Y42613A32S)。
摘 要:针对低截获概率(LPI)雷达信号在低信噪比(SNR)情况下识别效果不理想、网络模型复杂的问题,提出一种基于时频重排和多尺度残差网络的LPI雷达信号识别方法。该方法以魏格纳-维尔分布(WVD)为基础,通过时频重排算法提高信号的聚集性,得到信号的时频分布图像,输入到多尺度残差网络中完成信号的分类。通过构建多径莱斯衰落信道完成复杂电磁环境仿真,实验结果表明:所提方法在SNR为-8 dB时,对Costas、Frank、P1~P4等13类LPI雷达典型调制样式能达到94%的识别准确率,相比其他方法在低信噪比下具有更好的识别性能。In view of the problems of low probability of acquisition(LPI)radar signal recognition in low signalto-noise ratio(SNR)and complex network model,an LPI radar signal recognition method based on time-frequency reassignment and multi-scale residual network was proposed.The time-frequency reassignment approach is used to enhance the signal's aggregation based on the Wigner-Ville distribution(WVD).The resulting time-frequency distribution image is then fed into the multi-scale residual network to finish the signal's categorization.In addition,the complex electromagnetic environment simulation was completed by constructing a multi-path Rice-fading channel.According to the experimental results,when the SNR is−8 dB,the suggested approach can achieve 94%recognition accuracy for a total of 13 different types of typical LPI radar modulation patterns,including Costas,Frank,P1~P4,etc.Compared with other methods,it has better recognition performance at a low signal-to-noise ratio.
关 键 词:低截获概率雷达 信号识别 时频重排 多尺度残差网络 调制样式
分 类 号:TN974[电子电信—信号与信息处理]
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