基于瞬时特征参数和功率谱熵的联合调制识别

doi:10.16180/j.cnki.issn1007-7820.2022.11.015

电子科技 ›› 2022, Vol. 35 ›› Issue (11): 104-110.doi: 10.16180/j.cnki.issn1007-7820.2022.11.015

• • 上一篇

基于瞬时特征参数和功率谱熵的联合调制识别

谢爱平¹,张雨生²,刘莹²,何梓昂¹,高锐²

1.中国电子科技集团第二十九研究所电磁频谱研究中心,四川成都 610036
2.扬州大学信息工程学院,江苏扬州 225127

收稿日期:2021-10-29 出版日期:2022-11-15 发布日期:2022-11-11
作者简介:谢爱平(1984-),男,高级工程师。研究方向:电磁频谱数据分析、频谱资源管控等。|高锐(1986-),男,博士,副教授。研究方向:频谱感知、调制识别。
基金资助:
国家自然科学基金(61901408)

Joint Modulation Recognition Based on Instantaneous Feature and Power Spectrum Entropy

XIE Aiping¹,ZHANG Yusheng²,LIU Ying²,HE Ziang¹,GAO Rui²

1. Electromagnetic Spectrum Research Center, The 29th Research Institute of China Electronics Technology Group Corporation,Chengdu 610036,China
2. School of Information Engineering,Yangzhou University, Yangzhou 225127,China

Received:2021-10-29 Online:2022-11-15 Published:2022-11-11
Supported by:
National Natural Science Foundation of China(61901408)

摘要/Abstract

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。

关键词: 调制识别, 瞬时特征值, 特征参数, 功率谱熵

Abstract:

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.

Key words: modulation recognition, instantaneous eigenvalue, feature parameters, power spectrum entropy

中图分类号:

TN911.72

谢爱平,张雨生,刘莹,何梓昂,高锐. 基于瞬时特征参数和功率谱熵的联合调制识别[J]. 电子科技, 2022, 35(11): 104-110.

XIE Aiping,ZHANG Yusheng,LIU Ying,HE Ziang,GAO Rui. Joint Modulation Recognition Based on Instantaneous Feature and Power Spectrum Entropy[J]. Electronic Science and Technology, 2022, 35(11): 104-110.

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特征参数	功率谱熵值1	功率谱熵值2
门限值1	2.3	3.05
门限值2	2.4	2.95
门限值3	2.5	3.00

特征参数	门限值
T₁	0.310
T₂	0.223
T₃	0.315
T₄	0.225
T₅	2.400
T₆	2.950
T₇	0.440
T₈	0.380

基于瞬时特征参数和功率谱熵的联合调制识别

Joint Modulation Recognition Based on Instantaneous Feature and Power Spectrum Entropy

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