Cab Signal Denoising Process Based on Fully Convolutional Networks

XING Yulong; WANG Jian; ZHAO Huibing; ZHU Linfu

doi:10.3969/j.issn.0258-2724.20191111

Volume 56 Issue 2

Apr. 2021

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Journal of Southwest Jiaotong University > 2021 > 56(2): 444-450.

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

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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

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Cab Signal Denoising Process Based on Fully Convolutional Networks

doi: 10.3969/j.issn.0258-2724.20191111

XING Yulong^{1,2
,},
WANG Jian^{1,2
,
,},
ZHAO Huibing³,
ZHU Linfu^4,5

Received Date: 18 Nov 2019
Rev Recd Date: 18 May 2020

Available Online: 11 Jan 2021

Publish Date: 15 Apr 2021

Abstract

Abstract

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.
- railway signal,
- train operation control system,
- cab signal,
- signal processing,
- neural networks

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References(17)

References

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