Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/75828
Title: Brillouin optical time-domain analyzer assisted by support vector machine for ultrafast temperature extraction
Authors: Wu, H
Wang, L
Guo, N 
Shu, C
Lu, C 
Keywords: Brillouin optical time domain analyzer
Fiber optics sensors
Stimulated Brillouin scattering
Support vector machine
Issue Date: 2017
Publisher: Institute of Electrical and Electronics Engineers
Source: Journal of lightwave technology, 2017, v. 35, no. 19, p. 4159-4167 How to cite?
Journal: Journal of lightwave technology 
Abstract: Brillouin optical time-domain analyzer (BOTDA) assisted by support vector machine (SVM) for ultrafast temperature extraction is proposed and experimentally demonstrated. The temperature extraction is treated as a supervised classification problem and the Brillouin gain spectrum (BGS) is classified into each temperature class according to the support vectors and hyperplane of the SVM model after training. Ideal pseudo-Voigt curve-based BGS is used to train the SVM to get the support vectors and hyperplane. The performance of SVM is investigated in both simulation and experiment under various conditions for BGS collection. Both simulation and experiment results show that SVM is more robust to a wide range of signal-to-noise ratios, averaging times, pump pulse widths, frequency scanning steps, and temperatures. In addition to better accuracy, the processing speed for temperature extraction using SVM is 100 times faster than that using conventional Lorentzian curve and pseudo-Voigt curve fitting techniques in our experiment. The fast processing speed together with good accuracy and robustness makes SVM a highly competitive candidate for future high-speed BOTDA sensors
URI: http://hdl.handle.net/10397/75828
ISSN: 0733-8724
EISSN: 1558-2213
DOI: 10.1109/JLT.2017.2739421
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