Systems Engineering and Electronics ›› 2026, Vol. 48 ›› Issue (3): 1061-1071.doi: 10.12305/j.issn.1001-506X.2026.03.30

• Communications and Networks • Previous Articles    

Communication interference recognition method based on multi-feature fusion under low interference-to-noise ratio conditions

Hongyu TIAN1,2(), Songhu GE2,*, Yu GUO2, Zhongpu CUI2, Xiao LIANG2   

  1. 1. School of Electronic Information and Communications,Huazhong University of Science and Technology,Wuhan 430074,China
    2. National Key Laboratory of Electromagnetic Energy,Naval University of Engineering,Wuhan 430033,China
  • Received:2024-11-15 Online:2026-03-25 Published:2026-04-13
  • Contact: Songhu GE E-mail:hytian@hust.edu.cn

Abstract:

To address the issue of low recognition accuracy for communication interference signals under low interference-to-noise ratio conditions, which is caused by weak signal features and the insufficient representation capability of single feature, a communication interference signal recognition method based on YOLOv8 and multi-feature fusion networks is proposed. First, a feature image extraction method based on projection transformation is introduced. Based on the varying sensitivities of different features to different signals, bispectrum transform, Hilbert-Huang transform, and short-time Fourier transform, which possess strong complementary characteristics, are selected as recognition features, and they are unified into feature maps suitable for two-dimensional convolutional neural networks via projection transformation. Secondly, leveraging the powerful feature learning capability of the YOLOv8 network, deep discriminative information is extracted in parallel from the aforementioned three heterogeneous feature maps. Then, a decision-level fully connected fusion network is designed to perform weighted integration of the probability vectors output by YOLOv8, thereby achieving the final classification decision. Experimental results demonstrate that this method significantly enhances the recognition performance under low interference-to-noise ratio conditions. Particularly, under the adverse condition of a ?20 dB low interference-to-noise ratio, the recognition accuracy reaches 48.18%, representing improvements of 40.60% and 31.86% over traditional methods and single-feature methods, respectively.

Key words: interference recognition, multi-feature fusion, deep learning, YOLOv8

CLC Number: 

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