Intrusion location technology of Sagnac distributed fiber optical sensing system based on deep learning

Jinyi Wu, Rusheng Zhuo, Shengpeng Wan*, Xinzhong Xiong, Xinliang Xu, Bin Liu, Juan Liu, Jiulin Shi*, Jizhou Sun, Xingdao He, Qiang Wu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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For distributed fiber optical sensing based on Sagnac effect, the intrusion is usually located by notch frequency. However, the notch spectrum is the comprehensive result of the intrusion, so when multiple disturbances simultaneously intrude from different positions of the sensing fiber, it is impossible to establish a mathematical expression between the intrusion position and the notch frequency, this leads to the problem of multi-point intrusion localization. Therefore, in this paper, deep learning technology is used to locate multiple disturbing points in Sagnac distributed optical fiber sensing system, and the related specific technologies of deep learning appling to sagnac distributed optical fiber sensing are studied. First, according to the characteristics of the system, a network structure based on the regression probability distribution is proposed, second, a loss function is constructed. The results show that the trained model can realize the positioning of multiple and single intrusion points.
Original languageEnglish
Pages (from-to)13327-13334
Number of pages8
JournalIEEE Sensors Journal
Issue number12
Early online date2 Apr 2021
Publication statusPublished - 15 Jun 2021


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