• ISSN 0258-2724
  • CN 51-1277/U
  • EI Compendex
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Volume 54 Issue 4
Jul.  2019
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Journal of Southwest Jiaotong University  > 2019  >  54(4): 863-869, 878.
WEI Xing, WANG Rongrong, WEN Zongyi, DAI Lijun, HU Zhe. Influence of Bolt Relaxation of High-Speed Railway Sound Barrier on Fatigue Life[J]. Journal of Southwest Jiaotong University, 2023, 58(2): 373-380. doi: 10.3969/j.issn.0258-2724.20210060
Citation: HOU Jin, LÜ Zhiliang, XU Mao, WU Peijun, LIU Yuling, ZHANG Xiaoyu, CHENG Zeng. Combined Neural Networks Based on Deep Learning for Signal Detection in Aeronautical Communications[J]. Journal of Southwest Jiaotong University, 2019, 54(4): 863-869, 878. doi: 10.3969/j.issn.0258-2724.20180164
shu

Combined Neural Networks Based on Deep Learning for Signal Detection in Aeronautical Communications

doi: 10.3969/j.issn.0258-2724.20180164
  • Received Date: 10 Mar 2018
  • Rev Recd Date: 02 Jul 2018
    • Available Online: 08 Jul 2018
  • Publish Date: 01 Aug 2019
    Abstract
  • In order to increase the generality and accuracy of radio modulation recognition in complex radio propagation environment, a multiple feature combined convolutional network system based on deep learning is proposed. Carrier features were detected with front convolutional network in the first stage. Then, the signal filtered by the front CNN was converted into spectrograms with the proposed pre-process method. Finally, the lightweight backend convolutional network was designed to extract the time-frequency features of spectrograms. The networks, which run on TensorFlow, achieved 99.23% accuracy with real airport communication signals. The experiment indicates that the proposed networks could be applied in real-time airport radio detection.

     

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