Int J Performability Eng ›› 2020, Vol. 16 ›› Issue (6): 941-949.doi: 10.23940/ijpe.20.06.p12.941949

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Electromagnetic Signal Feature Fusion and Recognition based on Multi-Modal Deep Learning

Changbo Houa,b,*, Xiao Zhangb, and Xiang Chena   

  1. a State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System (CEMEE)Luoyang, 471003, China;
    b College of Information and Communication Engineering, Harbin Engineering University, Harbin, 150000, China
  • Submitted on ; Revised on ; Accepted on
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  • Supported by:
    This work is supported by the State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System (CEMEE2020K0102B), the Natural Science Foundation of Heilongjiang Province (No. JJ2019LH2398), and the Fundamental Research Funds for the Central Universities (No. 3072020CFT0801, 3072019CF0801, and 3072019CFM0802).

Abstract: Signal modulation recognition is the core of cognitive radio and spectrum sensing. With the rapid development and application of deep learning technology in recent years, multi-modal deep learning has become the mainstream of multi-modal machine learning. However, its usage in communication systems has not been well explored. This paper proposes a signal contour stellar images domain recognition method based on deep learning (DL) to achieve the problem of low recognition accuracy under low signal-to-noise ratio. A signal I/Q waveform domain recognition method based on deep complex-valued neural network is proposed to extract the amplitude and phase features of signals to achieve high-precision and high-robustness recognition of multiple signals. A multi-modal deep learning method is proposed to fuse image features, amplitudes, and phase features extracted by complex-valued neural networks to further improve the recognition accuracy and robustness of signals. Finally, the simulation results show the superiority of the scheme and prove that the scheme utilizes the complementarity between signal multi-modalities, removes the redundancy between the modes, and realizes the deep intelligent extraction of signal features, which can lead to a better signal recognition effect.

Key words: signal modulation recognition, multi-modal fusion, contour stellar images, complex-valued networks