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
Subject
Atomic and Molecular Physics, and Optics