Telecommunications Science ›› 2023, Vol. 39 ›› Issue (10): 74-84.doi: 10.11959/j.issn.1000-0801.2023185

• Research and Development • Previous Articles     Next Articles

Ultrashort wave satellite channel classification and recognition algorithm based on mirror filled spectrum and LA-ResNet50

Shang WU1,2, Lei SHEN1, Lijun WANG1,2,3, Ruxu ZHANG1, Xin HU1   

  1. 1 School of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310018,China
    2 The 36th Research Institute of China Electronics Technology Corporation, Jiaxing 314000, China
    3 Key Laboratory of Communication Information Control and Security Technology, Jiaxing 314000, China
  • Revised:2023-10-10 Online:2023-10-20 Published:2023-10-01
  • Supported by:
    The National Natural Science Foundation of China(62072121);The “Pioneer” and “Leading Goose” Research and development Program of Zhejiang(2023C01143)


In response to the classification and identification problems of 5 kHz channels, 25 kHz channels, broadband interference channels, narrowband interference channels, and single tone interference channels in the ultrashort wave frequency band, a classification and identification method for ultrashort wave channels based on mirror filled spectrum and LA-ResNet50 (LBP attention ResNet50) was proposed.The problem of difficulty in distinguishing between satellite channels and background noise under low signal-to-noise ratio, as well as the identification of signal channels and interference channels with similar characteristics, has been effectively solved.Firstly, the proposed method performs mirror symmetry on the ultrashort wave spectrum and fills it in, while blackening the edges of the spectrum to construct a mirror-filled spectrum, which improves the discrimination of different types of channel spectra.Then, channel attention was introduced into ResNet50 to focus the attention of the network model on the channel.Finally, a loss function based on cross entropy and local binary pattern (LBP) was proposed to improve the extraction effect of subtle texture features on signal channels and interference channels images.The proposed method based on mirror-filled spectrum and LA-ResNet50 has shown an improvement of 19.8%, 8.2%, 1.8%, and 0.8% in classification accuracy for ultrashort wave channels compared to the traditional method utilizing fast Fourier transform (FFT) spectrum thresholding, the YOLOv5s target detection and classification method based on mirror-filled spectrum, the Attention-ResNet50 method with attention mechanism based on mirror-filled spectrum, and the Transformer network method under a signal-to-noise ratio (SNR) of 10 dB.

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