基于支持向量机的典型宽带电磁干扰源识别  

Typical wideband e MI identification based on support vector machine

作　　者：朱峰[1] 蒋倩倩 林川[1] 杨啸 ZHU Feng;JIANG Qianqian;LIN Chuan;YANG Xiao(School of Electrical Engineering,Southwest Jiaotong University,Chengdu 611756,China)

机构地区：[1]西南交通大学电气工程学院,四川成都611756

出　　处：《系统工程与电子技术》2021年第9期2400-2406,共7页Systems Engineering and Electronics

基　　金：国家自然科学基金(60831001);国防基金(9140A31010109HK0101)资助课题。

摘　　要：由于民航周围电磁环境复杂,一旦产生电磁干扰(electromagnetic interference,EMI),就不易被排查,特别是随机性较强的宽带干扰。对此,提出一种基于支持向量机(support vector machine,SVM)的干扰源识别方法。通过实时测量干扰信号的频谱数据,并分析其特点,选择包络因子、频谱能量、频谱峰值、均值和方差5个特征向量,用主成分分析法降低数据冗余程度,最后采用SVM来判断干扰源类型。仿真结果证明,所提算法能有效识别6类典型机场宽带干扰源,识别精度可达98.33%。Due to the complex electromagnetic environment around civil aviation,once the electromagnetic interference (EMI)is produced,it is not easy to be investigated,especially the random strong wideband interference.For wideband,an interference source recognition method based on support vector machine (SVM)is proposed.By measuring the spectral data of the signal in real time and analyzing its characteristics,five features of the evenlope factor,energy,peak value,mean and variance are selected as feature vectors,and principal component analysis is used to reduce data redundancy,finally,the type of the interference source is determined by SVM.Simulation results show that the identification algorithm proposed in this paper can effectively identify 6 types of wideband interference,and the identification accuracy is up to 98.33%.

关 键 词：电磁干扰 信号处理 特征提取 支持向量机 

分 类 号：V243.1[航空宇航科学与技术—飞行器设计]