基于熵特征的调频引信目标与干扰信号识别
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摘要
针对调频引信较难抑制的调幅扫频类干扰,提出了一种基于熵特征的目标与干扰信号分类识别方法。比较目标和干扰信号作用下引信检波信号时域和频域特征,提取检波信号的香农熵和奇异谱熵,通过Kruskal-Wallis检验方法验证了特征参量的有效性,并利用支持向量机分类器对目标信号和干扰信号进行了分类识别。仿真实验结果表明:在支持向量机核函数参数最优时,该方法的分类识别正确率达到98.954%,有效提高了调频引信的抗扫频式干扰能力。
A classification and recognition method of target and jamming signal based on entropy features is proposed to restrain the AM frequency-sweeping jamming of FM fuze. The time domain and frequency domain characteristics of output signal of fuze detector under the action of target and jamming signal are compared,the Shannon entropy and singular spectrum entropy of fuze detection output signal are extracted and verified to be valid by Kruskal-Wallis test method,and the support vector machine is used for classification and recognition of target signal and jamming signals. The experimental results show that the correct recognition rate of the proposed method is 98. 954%,and it can effectively improve the ability of the anti-frequency sweeping jamming of FM fuze.
A classification and recognition method of target and jamming signal based on entropy features is proposed to restrain the AM frequency-sweeping jamming of FM fuze. The time domain and frequency domain characteristics of output signal of fuze detector under the action of target and jamming signal are compared,the Shannon entropy and singular spectrum entropy of fuze detection output signal are extracted and verified to be valid by Kruskal-Wallis test method,and the support vector machine is used for classification and recognition of target signal and jamming signals. The experimental results show that the correct recognition rate of the proposed method is 98. 954%,and it can effectively improve the ability of the anti-frequency sweeping jamming of FM fuze.
引文
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[11]李坤,刘鹏,吕雅洁,等.基于Spark的LIBSVM参数优选并行化算法[J].南京大学学报:自然科学,2016,52(2):343-352.LI Kun,LIU Peng,LYU Ya-jie,et al.The parallel algorithms for LIBSVM parameter optimization based on Spark[J].Journal of Nanjing University:Natural Sciences,2016,52(2):343-352.(in Chinese)
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[2]刘景萍,赵惠昌,黄文良.无线电引信的有源干扰机理[J].制导与引信,2001,22(4):50-52.LIU Jing-ping,ZHAO Hui-chang,HUANG Wen-liang.Active jamming mechanism of radio fuzes[J].Guidance&Fuze,2001,22(4):50-52.(in Chinese)
[3]李靖超.基于三维熵特征的雷达信号识别[J].上海电机学院学报,2015,18(3):136-140.LI Jing-chao.Radar signal recognition based on three dimensional entropy features[J].Journal of Shanghai Dianji University,2015,18(3):136-140.(in Chinese)
[4]白航,赵拥军,沈伟,等.基于时频分布Rényi熵特征的雷达辐射源识别[J].电路与系统学报,2013,18(1):437-442.BAI Hang,ZHAO Yong-jun,SHEN Wei,et al.Radar emitter recognition based on Rényi entropy of time-frequency distribution[J].Journal of Circuits and Systems,2013,18(1):437-442.(in Chinese)
[5]谢平,杜义浩,黄双峰.基于信息熵的复杂机械系统非线性特征提取方法研究[J].振动与冲击,2008,27(7):135-137.XIE Ping,DU Yi-hao,HUANG Shuang-feng.Research on nonlinear feature extracting method of complex mechanical system based on information entropy[J].Journal of Vibration and Shock,2008,27(7):135-137.(in Chinese)
[6]马建华,刘宏伟,保铮.固定翼飞机和直升机的分类方法研究[J].现代雷达,2004,26(12):45-48.MA Jian-hua,LIU Hong-wei,BAO Zheng.Research on helicopter and fixed-wing aircraft classification[J].Modern Radar,2004,26(12):45-48.(in Chinese)
[7]蒋正新,施国梁.矩阵理论及应用[M].北京:北京航空学院出版社,1988:87-95.JIANG Zheng-xin,SHI Guo-liang.Matrix theory and application[M].Beijing:Beijing Aeronautical Institute Press,1988:87-95.(in Chinese)
[8]高清清,贾民平.基于EEMD的奇异谱熵在旋转机械故障诊断中的应用[J].东南大学学报:自然科学版,2011,41(5):998-1001.GAO Qing-qing,JIA Min-ping.EEMD method based singular value spectral entropy in fault diagnosis of rotating machinery[J].Journal of Southeast University:Natural Science Edition,2011,41(5):998-1001.(in Chinese)
[9]陶卿,刘欣,唐升平,等.基于支持向量机的弹道识别及其在雷达弹道外推中的应用[J].兵工学报,2005,26(3):308-311.TAO Qing,LIU Xin,TANG Sheng-ping,et al.Support vector machine-based trajectory recognition with applications in the adjustment of radar trajectory prediction[J].Acta Armamentarii,2005,26(3):308-311.(in Chinese)
[10]陈冰梅,樊晓平,周志明,等.支持向量机原理及展望[J].制造业自动化,2010,32(12):136-138.CHEN Bing-mei,FAN Xiao-ping,ZHOU Zhi-ming,et al.The principle and prospect of support vector machine[J].Manufacturing Automation,2010,32(12):136-138.(in Chinese)
[11]李坤,刘鹏,吕雅洁,等.基于Spark的LIBSVM参数优选并行化算法[J].南京大学学报:自然科学,2016,52(2):343-352.LI Kun,LIU Peng,LYU Ya-jie,et al.The parallel algorithms for LIBSVM parameter optimization based on Spark[J].Journal of Nanjing University:Natural Sciences,2016,52(2):343-352.(in Chinese)
[12]陈慧玲.调频连续波无线电引信目标与干扰信号识别方法[D].北京:北京理工大学,2015.CHEN Hui-ling.On recognition of target and interference signal for FM-CM radio proximity fuze[D].Beijing:Beijing Institute of Technology,2015.(in Chinese)