Recognition and localization of asymmetric spectra in FBG sensing networks

Author:

Hu Jinhua1ORCID,Di Kangjian,Ren Danping1,Deng Yujing,Zhao Jijun1ORCID

Affiliation:

1. Hebei Key Laboratory of Security Protection Information Sensing and Processing

Abstract

We propose a deep learning demodulation method based on a long short-term memory (LSTM) neural network for fiber Bragg grating (FBG) sensing networks. Interestingly, we find that both low demodulation error and distorted spectrum recognition are realized using the proposed LSTM-based method. Compared with conventional demodulation methods, including Gaussian-fitting, convolutional neural network, and the gated recurrent unit, the proposed method improves the demodulation accuracy being close to 1 pm and achieves a demodulation time of 0.1s for 128-FBG sensors. Furthermore, our approach can realize 100% accuracy of distorted spectra recognition and complete the location of spectra with spectrally encoded FBG sensors.

Funder

National Natural Science Foundation of China

Scientific Research Project of the Department of Education of Hebei Province, China

Natural Science Foundation of Hebei Province

Publisher

Optica Publishing Group

Subject

Atomic and Molecular Physics, and Optics

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