Recognition and suppression of phase-sensitive OTDR interference fading based on machine learning

Junhong Wang; Yu Wang; Qin Bai; Xin Liu; Baoquan Jin

doi:10.1117/12.3046340

13 December 2024 Recognition and suppression of phase-sensitive OTDR interference fading based on machine learning

Junhong Wang, Yu Wang, Qin Bai, Xin Liu, Baoquan Jin

Author Affiliations +

Proceedings Volume 13498, AOPC 2024: Optoelectronics Testing and Measurement; 1349806 (2024) https://doi.org/10.1117/12.3046340
Event: Applied Optics and Photonics China 2024 (AOPC2024), 2024, Beijing, China

Abstract

In phase-sensitive optical time domain reflectometer (Φ-OTDR), the existence of interference fading signal leads to misjudgment and missing judgment in vibration sensing. In this paper, a fading recognition model of back propagation (BP) neural network is constructed, features are extracted from beat frequency generated based on frequency shift delay loop. A fading recognition accuracy of 91.82% is achieved. Rotated-vector-sum algorithm is introduced to aggregate signal labelled non-fading. Vibration location and phase demodulation is realized and on this basis, this method improves the interference fading suppression probability.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Junhong Wang, Yu Wang, Qin Bai, Xin Liu, and Baoquan Jin "Recognition and suppression of phase-sensitive OTDR interference fading based on machine learning", Proc. SPIE 13498, AOPC 2024: Optoelectronics Testing and Measurement, 1349806 (13 December 2024); https://doi.org/10.1117/12.3046340

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