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

Abstract

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

CLC Number:

TN911.72

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.

Figures/Tables 7

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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

Joint Modulation Recognition Based on Instantaneous Feature and Power Spectrum Entropy

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