• ISSN 0258-2724
  • CN 51-1277/U
  • EI Compendex
  • Scopus
  • Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
  • Chinese S&T Journal Citation Reports
  • Chinese Science Citation Database
Volume 56 Issue 2
Apr.  2021
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Article Contents
XING Yulong, WANG Jian, ZHAO Huibing, ZHU Linfu. Cab Signal Denoising Process Based on Fully Convolutional Networks[J]. Journal of Southwest Jiaotong University, 2021, 56(2): 444-450. doi: 10.3969/j.issn.0258-2724.20191111
Citation: XING Yulong, WANG Jian, ZHAO Huibing, ZHU Linfu. Cab Signal Denoising Process Based on Fully Convolutional Networks[J]. Journal of Southwest Jiaotong University, 2021, 56(2): 444-450. doi: 10.3969/j.issn.0258-2724.20191111

Cab Signal Denoising Process Based on Fully Convolutional Networks

doi: 10.3969/j.issn.0258-2724.20191111
  • Received Date: 18 Nov 2019
  • Rev Recd Date: 18 May 2020
  • Available Online: 11 Jan 2021
  • Publish Date: 15 Apr 2021
  • Since cab signals extract information from track circuits as the running token, its decoding performance has a direct impact on the reliability and security of train operation control system. However, as it is inevitable that a lot of noise and interference will mix into the cab signal during operation, it is necessary to denoise before decoding in order to improve demodulation accuracy. To this end, a raw waveform-based fully convolutional network (FCN) for denoising is proposed in an end-to-end manner, which denoises the cab signal in time domain directly and improves the signal-to-noise ratio (SNR). This proposed network is validated through simulation and measured data. The experimental results show that compared with the traditional spectrum-based denoising methods, this method has a more significant effect on in-band interference; FCN can improve the SNR of cab signals by 8~14 dB and effectively reduce the in-band interference.

     

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