Electronic Science and Technology ›› 2022, Vol. 35 ›› Issue (11): 104-110.doi: 10.16180/j.cnki.issn1007-7820.2022.11.015

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Joint Modulation Recognition Based on Instantaneous Feature and Power Spectrum Entropy

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

  1. 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)


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