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.

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