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

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.