联合多维特征的干扰识别技术研究

联合多维特征的干扰识别技术研究
梁金弟;程郁凡;杜越;王鹏宇
【期刊名称】《信号处理》
【年(卷),期】2017(33)12
【摘要】干扰识别是通信电子防御系统的重要组成部分,针对复杂电磁环境的干扰识别问题,本文研究了联合多维特征的干扰识别技术,从时域、频域和变换域提取了一组对干扰参数、噪声敏感度低且复杂度较低的特征参数,给出了联合多维特征且基于决策树和支持向量机(Support Vector Machine,SVM)的两种干扰识别器结构,并进行了干扰识别性能仿真对比分析.仿真结果表明,这两种识别器对典型电磁干扰均具有良好的识别性能,对于瞄准式干扰、部分频带噪声干扰、噪声调频干扰和脉冲干扰,二者识别性能很接近;对于梳状多音干扰和线性调频干扰,SVM识别器比决策树识别器具有更好的识别性能.%Interference recognition is an important part of the communication electronic defense system.As for the interference recognition problem of the complex electromagnetic environment,this paper studies an interference recognition technology based on the joint multi-dimensional features,extracts a set of features which are less sensitive to interference parameters and noise,and are less complex from the time domain,frequency domain and transform domain.With the joint multi-dimensional features,two kinds of interference identifier structures based on the decision tree and support vector machine (SVM) are proposed,and interference recognition performance simulation is carried out.The simulation results show that these two
identifiers have good recognition performance for typical electromagnetic interference,they have similar performance for the aiming interference,the partial band noise interference,the noise frequency modulated interference and the pulse interference;for the comb shaped multi-tone interference and linear frequency modulated interference,the SVM recognizer has better recognition performance than the decision tree recognizer.
【总页数】7页(P1609-1615)
【作者】梁金弟;程郁凡;杜越;王鹏宇
【作者单位】电子科技大学通信抗干扰技术国家级重点实验室,四川成都611731;通信网信息传输与分发技术重点实验室,河北石家庄050081;电子科技大学通信抗干扰技术国家级重点实验室,四川成都611731;电子科技大学通信抗干扰技术国家级重点实验室,四川成都611731;电子科技大学通信抗干扰技术国家级重点实验室,四川成都611731
【正文语种】中文
【中图分类】TN911.7
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