Extraction of Brillouin frequency shift in Brillouin distributed fiber sensors by neighbors-based machine learning

Huan Zheng

doi:10.1117/12.2573346

10 October 2020 Extraction of Brillouin frequency shift in Brillouin distributed fiber sensors by neighbors-based machine learning

Huan Zheng

Author Affiliations +

Proceedings Volume 11554, Advanced Sensor Systems and Applications X; 115540G (2020) https://doi.org/10.1117/12.2573346
Event: SPIE/COS Photonics Asia, 2020, Online Only

Abstract

We propose a novel method to extract Brillouin frequency shift (BFS) from Brillouin Gain Spectrum (BGS) in Brillouin distributed fiber sensors. The method is based on machine learning of nearest neighbors. In order to find the BFS from the BGS, we design two datasets, one for storing all possible BGS, and the other for storing the corresponding BFS. By comparing the given BGS with the dataset of BGS, we get the minimal kth BFS. The BFS of the given BGS is determined by voting of the kth BFS. By simulations, we compare the performance of both neighbor-based machine learning and curve-fitting. The results show that the method of neighbor-based machine learning is more robust under a wide range of signal-to-noise ratios, pump pulse widths, and frequency scanning steps. The extracting method of neighbor-based machine learning is highly competitive for future Brillouin distributed fiber sensors.

Conference Presentation

Citation Download Citation

Huan Zheng "Extraction of Brillouin frequency shift in Brillouin distributed fiber sensors by neighbors-based machine learning", Proc. SPIE 11554, Advanced Sensor Systems and Applications X, 115540G (10 October 2020); https://doi.org/10.1117/12.2573346

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