We propose a novel approach to the recognition of particular classes of non-conventional events in signals from phase-sensitive optical time-domain-reflectometry-based sensors. Our algorithmic solution has two main features: filtering aimed at the de-nosing of signals and a Gaussian mixture model to cluster them. We test the proposed algorithm using experimentally measured signals. The results show that two classes of events can be distinguished with the best-case recognition probability close to 0.9 at sufficient numbers of training samples.
@article{arxiv.1509.05998,
title = {Gaussian mixture model for event recognition in optical time-domain reflectometry based sensing systems},
author = {Aleksey Fedorov and Maxim Anufriev and Andrey Zhirnov and Konstantin Stepanov and Evgeniy Nesterov and Dmitry Namiot and Valery Karasik and Alexey Pnev},
journal= {arXiv preprint arXiv:1509.05998},
year = {2016}
}