2024 04 v.47;No.316 1-7
基于改进MobileV3Net的脉冲雷达干扰识别方法
基金项目(Foundation):
国家自然科学基金,项目编号：62071137
邮箱(Email):
DOI:
10.16426/j.cnki.jcdzdk.2024.04.001
中文作者单位:
哈尔滨工程大学;先进船舶通信与信息技术工信部重点实验室;
摘要(Abstract):
随着现代电子战的飞速发展，基于数字射频存储器转发的新型干扰层出不穷，如何快速有效地识别这类干扰成为现今研究的热点问题。针对于此，提出了一种基于改进MobileV3Net的脉冲雷达干扰识别研究，使用MobileV3Net作为基本网络框架，添加了动态卷积模块和高效通道注意力模块，实现了自动提取特征的小样本下8类干扰的有效识别。仿真结果表明，该网络的训练时间大大减少，且在轻量训练样本下依然能保持95%以上的识别准确率，在-10～10 dB下，平均识别率在99%以上，证明该方法具有更强的鲁棒性，更高的准确度，更好的轻量性。
关键词(KeyWords):
雷达有源干扰;;时频域分析;;卷积神经网络;;动态卷积
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[2] 周红平，王子伟，郭忠义.雷达有源干扰识别算法综述[J].数据采集与处理，2022,37(1):1-20.
[3] 王雨鑫.新型雷达干扰识别方法研究[D].哈尔滨：哈尔滨工程大学，2021.
[4] WANG Y,SUN B,WANG N.Recognition of radar active-jamming through convolutional neural networks[J].The Journal of Engineering,2019(21):7695-7697.
[5] LI M,REN Q,WU J.Interference classification and identification of TDCS based on improved convolutional neural network [J].Journal of Physics Conference Series,2020,1651(1):012155.
[6] 郭治锐，鲁军，刘磊，等.基于 AlexNet的雷达干扰识别方法研究[J].电光与控制，2021,28(9):49-53.
[7] 刘国满，聂旭娜.一种基于卷积神经网络的雷达干扰识别算法[J].北京理工大学学报，2021,41(9):990-998.
[8] ZHANG Y B.Technology of smart noise jamming based on multiplication modulation[C]//2011 International Conference on Electric Information and Control Engineering.Wuhan:IEEE Press,2011:1-3.
[9] 黄大通，邢世其，李永祯，等.基于乘积调制的SAR灵巧干扰方法[J].系统工程与电子技术，2021,43(11):3160-3168.
[10] 崔国龙，李乾，刘加换，等.一种SMSP干扰和C&I干扰的检测方法：201510520716.9[P].2015-08-24.
[11] WANG X S,LIU J C,ZHANG W M,et al.Mathematic principles of interrupted-sampling repeater jamming (ISRJ)[J].Science in China(Series F:Information Sciences),2007,50(1):113-123.
[12] LI Y P,YING X,TANG B.SMSP jamming identification based on matched signal transform[C]//2011 International Conference on Computational Problem-Solving (ICCP).Chengdu,China:IEEE Press,2011:182-185.
[13] 赵淑君，农林舒真，黄晓文，等.基于新阈值函数的小波去噪研究[J].科学技术创新，2022(18):34-37.
[14] 郭立民，陈鑫，陈涛.基于AlexNet模型的雷达信号调制类型识别[J].吉林大学学报(工学版),2019,49(3):1000-1008.
[15] KIM W,JUNG W S,CHOI H K.Lightweight driver monitoring system based on multi-task mobilenets[J].Sensors,2019,19(14):3200-3217.
[16] SANDLER M,HOWARD A,ZHU M L,et al.Mobile-netv2:inverted residuals and linear bottlenecks[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Salt Lake City,USA:IEEE Press,2018:4510-4520.
[17] LIU T,LUO R,XU L,et al.Spatial channel attention for deep convolutional neural networks[J].Mathematics,2022,10(10):1750.
[18] CHEN Y,DAI X,LIU M,et al.Dynamic convolution:attention over convolution kernels[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.Seattle,WA,USA:IEEE Press,2020:11030-11039.
[19] HOWARD A,SANDLER M,CHU G,et al.Searching for mobilenetv3[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision.Seoul,Korea(South):IEEE,2019:1314-1324.
基本信息:
DOI：10.16426/j.cnki.jcdzdk.2024.04.001
中图分类号:TN974
引用信息:
[1]郭立民,鄂璟仪,黄文青.基于改进MobileV3Net的脉冲雷达干扰识别方法[J].舰船电子对抗,2024,47(04):1-7.DOI:10.16426/j.cnki.jcdzdk.2024.04.001.
基金信息:
国家自然科学基金,项目编号：62071137
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