基于SVM的干扰样式选择
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摘要
建立了空-空对抗场景机载多功能火控雷达小样本干扰规则库,针对当前雷达干扰样式选择实时性及正确率不能满足认知电子战多功能雷达干扰需求问题,提出一种基于小样本干扰规则库的干扰样式选择模型及适用于该模型的支持向量机(SVM)干扰样式选择方法。将SVM理论应用干扰样式选择,利用非线性映射将输入测试数据的内积变换到特征空间的内积,其出色的学习及泛化能力,较好地解决了小样本、非线性实际问题,仿真实验验证了该方法的有效性。
A small sample jamming rule base for airborne multi-function fire control radar in air-to-air confrontation scene is established.For the problem that the radar jamming style selection real-time and correct rate cannot meet the multi-function radar jamming demand of cognitive electronic warfare.The jamming style selection model based on the jamming rule base and the support vector machine(SVM)jamming style selection method applicable to the model are proposed.The SVM theory is applied to the jamming style selection,and the inner product of the input test data is transformed into the inner product of the feature space by using the nonlinear mapping.The small sample and nonlinear practical problems are solved for its excellent learning and generalization ability.Simulate experiments verify the effectiveness of the method.
A small sample jamming rule base for airborne multi-function fire control radar in air-to-air confrontation scene is established.For the problem that the radar jamming style selection real-time and correct rate cannot meet the multi-function radar jamming demand of cognitive electronic warfare.The jamming style selection model based on the jamming rule base and the support vector machine(SVM)jamming style selection method applicable to the model are proposed.The SVM theory is applied to the jamming style selection,and the inner product of the input test data is transformed into the inner product of the feature space by using the nonlinear mapping.The small sample and nonlinear practical problems are solved for its excellent learning and generalization ability.Simulate experiments verify the effectiveness of the method.
引文
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[2] Guerci JR.Cognitive radar:a knowledge-aided fully adaptive approach[C]∥Radar Conference.IEEE,2010:1365-1370.
[3] Yuan RF,Gan RB,Tang GF,et al.Range-doppler and anti-interference performance of cognitive radar detection waveform[C]∥2015 12th IEEE International Conference on Electronic Measurement&Instruments,2015:607-612.
[4] Krzysztof K,Micha S,Marcin Z,et al.Cognitive systems in electronic warfare[C]∥XI Conference on Reconnaissance and Electronic Warfare Systems, 2017,1041802:1-7.
[5] Peng HH,Chen CK,Hsueh CS.Design and implementation of intelligent electronic warfare decision making algorithm[C]∥Signal Processing,Sensor/Information Fusion,and Target Recognition XXVI,2017:102001L:1-5.
[6] Sameer A.Cognitive electronic warfare system[C]∥Cognitive Radio Network,2016:14.
[7]李振初.人工智能技术在电子战中的应用[J].电子对抗技术,1988(2):27-39.
[8] DARPA.Behavior Learning for Adaptive Electronic Warfare[R/OL].[2010-10-06].https:∥www.fbo.gov.
[9] DARPA.Adaptive Radar Countermeasures[R/OL].[2012-8-27].https:∥www.fbo.gov.
[10]Air Force.Cognitive Jammer[EB/OL].[2010-1-20].https:∥www.fbo.gov.
[11] DARPA.Communications Under Extreme RF Spectrum Condi-tions[R/OL].[2010-9-10].https:∥www.fbo.gov.
[12]李云杰,朱云鹏,高梅国.基于Q-学习算法的认知雷达对抗过程设计[J].北京理工大学学报,2015,35(11):1194-1199.
[13]唐文龙,张剑云,王冰川,等.干扰样式选择方法研究[J].现代雷达,2017,39(1):72-76.
[14]赖中安,周刚峰.矩阵博弈应用于雷达有源干扰策略选择的研究[J].航天电子对抗,2010,26(5):16-18.
[15]张永顺.复杂电磁环境下基于博弈论的机载雷达对抗仿真研究[D].西安电子科技大学,2011.
[16]宋玉珍,刘炼.复杂电磁环境下基于博弈论的雷达对抗性能研究[J].中国雷达,2012(4):1-3.
[17]刘清,王兴华,王星,等.干扰方式选择方法的研究[J].现代防御技术,2011,39(4):50-54.
[18]周脉成.基于博弈论的雷达干扰决策技术研究[D].西安电子科技大学,2014.
[19]陈凯.对相控阵雷达的智能干扰决策技术研究[D].西安电子科技大学,2012.
[20]Zhang Ge Xiang,Jin Wei Dong,Hu Lai Zhao.Resemblance coefficient based intrapulse feature extraction approach for radar emitter signals[J].Chinese Journal of Electronics,2005,14(2):337-341.
[21]Lv Tiejun,Guo Shuangbing,Xiao Xianci.Study on fractal features of modulation signals[J].Science in China,2001,44(2):152-159.
[22]韩俊,何明浩,朱振波,等.基于复杂度特征的未知雷达辐射源信号分选[J].电子与信息学报,2009,31(11):2552-2556.
[23]Skolnik MI.雷达手册[M].北京:电子工业出版社,2010.
[24]中航工业雷达与电子设备研究院.机载雷达手册.第4版[M].北京:国防工业出版社,2013.
[25]李航.统计学习方法[M].北京:清华大学出版社,2016.
[26]Vapnik VN.统计学习理论[M].北京:电子工业出版社,2009.
[27]Shieh CS,Lin CT.A vector neural network for emitter identification[J].IEEE Transactions on Antennas&Propagation,2002,50(8):1120-1127.