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arXiv · 1509.05998

Gaussian mixture model for event recognition in optical time-domain reflectometry based sensing systems

Aleksey Fedorov·Maxim Anufriev·Andrey Zhirnov·Konstantin Stepanov·Evgeniy Nesterov·Dmitry Namiot·Valery Karasik·Alexey Pnev
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Abstract

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.

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Aleksey Fedorov, Maxim Anufriev, Andrey Zhirnov, Konstantin Stepanov, Evgeniy Nesterov, Dmitry Namiot, Valery Karasik, Alexey Pnev. 2016-03-20. Gaussian mixture model for event recognition in optical time-domain reflectometry based sensing systems. https://doi.org/10.1063/1.4944417

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