Journal
OPTICAL ENGINEERING
Volume 54, Issue 5, Pages -Publisher
SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS
DOI: 10.1117/1.OE.54.5.055104
Keywords
vibrational intrusions; wavelet packet; Shannon entropy; radial basis function; classification; fiber optic; Sagnac interferometer
Categories
Funding
- National Key Technology RD Program [2013BAK02B03]
- National Natural Science Foundation of China [61107077]
- National Instrumentation Program (NIP) [2012YQ150213, 2014YQ090709]
- Key Projects of Shanghai Science and Technology Commission [14DZ2281200, 14511101800, 13231200203]
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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. (C) 2015 Society of PhotoOptical Instrumentation Engineers (SPIE)
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