Brillouin optical time domain analyzer enhanced by artificial/deep neural networks

Liang Wang, Biwei Wang, Chao Jin, Nan Guo, Changyuan Yu, Chao Lu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

2 Citations (Scopus)


We report our recent studies on the use of Neural Networks to process the measured Brillouin gain spectrum (BGS) from Brillouin Optical Time Domain Analyzer (BOTDA) and extract temperature information along fiber under test (FUT). Artificial Neural Network (ANN) is trained with ideal Lorentizian BGS before it is used for temperature extraction. Its performance is evaluated by comparison to conventional curve fitting techniques, showing better accuracy especially at large frequency scanning step during the acquisition of BGSs. We have also applied advanced hierarchical Deep Neural Network (DNN) in BOTDA for temperature extraction to improve the training and testing efficiency. We believe that ANN/DNN can be attractive tools for direct temperature or strain extraction in BOTDA system with high accuracy.
Original languageEnglish
Title of host publicationICOCN 2017 - 16th International Conference on Optical Communications and Networks
Number of pages3
ISBN (Electronic)9781538632734
Publication statusPublished - 27 Nov 2017
Event16th International Conference on Optical Communications and Networks, ICOCN 2017 - Wuzhen, China
Duration: 7 Aug 201710 Aug 2017


Conference16th International Conference on Optical Communications and Networks, ICOCN 2017


  • Distributed fiber sensor
  • Neural networks
  • Stimulated Brillouin scattering

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Instrumentation
  • Atomic and Molecular Physics, and Optics

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