摘要
针对传统的辐射源个体识别方法在低信噪比环境下识别性能不佳的问题,提出了一种空中目标辐射源的个体识别方法,该方法利用经验模态分解和变分模态分解得到信号不同频率的模态分量,将各模态分量的多尺度排列熵作为特征,利用主成分分析对数据进行降维,并采用支持向量机分类器进行辐射源个体识别。仿真结果表明,该方法对相位噪声、频率漂移以及谐波失真等细微特征的识别性能明显优于传统方法,并具有良好的抗噪性。
For the problem of the poor performance of traditional specific emitter identification methods in low signal to noise ratio (SNR) environments,a method of specific emitter identification for the aerial target is proposed.Empirical mode decomposition and variational mode decomposition are employed to obtain modal components of different frequencies of the signals,and multi-scale entropy of each modal component is taken as features.The principal component analysis is used to reduce the dimensions of the features,and the support vector machine is used as a classifier to identify the specific emitter identification.Simulation results show that the proposed method has better recognition and anti-noise performance for fine features such as phase noise,frequency drift and harmonic distortion than the traditional methods.
作者
刘明骞
颜志文
张俊林
LIU Mingqian;YAN Zhiwen;ZHANG Junlin(State Key Laboratory of Integrated Service Networks,Xidian University,Xi’an 710071,China)
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2019年第11期2408-2415,共8页
Systems Engineering and Electronics
基金
国家自然科学基金（61501348,61801363）
陕西省重点研发计划（2019GY-043）
中国博士后科学基金（2017M611912）
中央高校基本科研业务费专项资金（JB180106）
高等学校学科创新引智计划（B08038）资助课题
关键词
辐射源个体识别
细微特征
模态分解
多尺度排列熵
支持向量机
specific emitter identification(SEI)
fine features
mode decomposition
multi-scale permutation entropy
support vector machine (SVM)
