Improved wavelet packet classification algorithm for vibrational intrusions in distributed fiber-optic monitoring systems

Bingjie Wang; Shaohua Pi; Qi Sun; Bo Jia

doi:10.1117/1.OE.54.5.055104

20 May 2015 Improved wavelet packet classification algorithm for vibrational intrusions in distributed fiber-optic monitoring systems

Bingjie Wang, Shaohua Pi, Qi Sun, Bo Jia

Author Affiliations +

Optical Engineering, Vol. 54, Issue 5, 055104 (May 2015). https://doi.org/10.1117/1.OE.54.5.055104

Abstract

An improved classification algorithm that considers multiscale wavelet packet Shannon entropy is proposed. Decomposition coefficients at all levels are obtained to build the initial Shannon entropy feature vector. After subtracting the Shannon entropy map of the background signal, components of the strongest discriminating power in the initial feature vector are picked out to rebuild the Shannon entropy feature vector, which is transferred to radial basis function (RBF) neural network for classification. Four types of man-made vibrational intrusion signals are recorded based on a modified Sagnac interferometer. The performance of the improved classification algorithm has been evaluated by the classification experiments via RBF neural network under different diffusion coefficients. An 85% classification accuracy rate is achieved, which is higher than the other common algorithms. The classification results show that this improved classification algorithm can be used to classify vibrational intrusion signals in an automatic real-time monitoring system.

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Bingjie Wang, Shaohua Pi, Qi Sun, and Bo Jia "Improved wavelet packet classification algorithm for vibrational intrusions in distributed fiber-optic monitoring systems," Optical Engineering 54(5), 055104 (20 May 2015). https://doi.org/10.1117/1.OE.54.5.055104

Published: 20 May 2015

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