摘要
针对传统的瞬时特征参数识别法在低信噪比下识别信号种类少、识别率低等问题,文中提出了一种基于瞬时特征参数与功率谱熵联合的调制识别方法。该方法通过改进后的瞬时幅度及相位特征参数对调制信号进行识别,并引入了功率谱熵特征参数,可进一步实现对更多信号的类内识别。在信号类内识别方面,采用决策树分类方法,选取合适的门限值对常用的9种数字调制信号{ASK、4ASK、2FSK、4FSK、8FSK、BPSK、QPSK、8PSK、16QAM}进行识别分类。蒙特卡洛实验结果表明,相较于现有的识别方法,文中所提出的方法增加了识别信号的种类,还提高了低信噪比情况下的信号识别准确率。
To solve the problem that the traditional instantaneous characteristic parameter recognition method has few signal types and low recognition rate under low SNR,a modulation recognition method based on the combination of instantaneous characteristic parameter and power spectrum entropy is proposed in this study.The improved instantaneous amplitude and phase characteristic parameters are used to identify the modulation signals,and the power spectrum entropy characteristic parameters are introduced to further realize the in-class recognition of more signals.The decision tree classification method is used to identify and classify the 9 common digital modulation signals{ASK,4ASK,2FSK,4FSK,8FSK,BPSK,QPSK,8PSK,16QAM}with appropriate threshold values.Monte Carlo experiment results show that compared with the existing recognition methods,the proposed method increases the number of signal types,and improves the signal recognition accuracy in the case of low SNR.
作者
谢爱平
张雨生
刘莹
何梓昂
高锐
XIE Aiping;ZHANG Yusheng;LIU Ying;HE Ziang;GAO Rui(Electromagnetic Spectrum Research Center,The 29th Research Institute of China Electronics Technology Group Corporation,Chengdu 610036,China;School of Information Engineering,Yangzhou University,Yangzhou 225127,China)
出处
《电子科技》
2022年第11期104-110,共7页
Electronic Science and Technology
基金
国家自然科学基金(61901408)。
关键词
调制识别
瞬时特征值
特征参数
功率谱熵
modulation recognition
instantaneous eigenvalue
feature parameters
power spectrum entropy
