Optimized Support Vector Machine Assisted BOTDA for Temperature Extraction with Accuracy Enhancement

Abstract

Brillouin optical time domain analyzer (BOTDA) assisted by optimized support vector machine (SVM) algorithm for accurate temperature extraction is presented and experimentally demonstrated. Three typical intelligent optimization algorithms, particle swarm optimization algorithm, genetic algorithm and firefly algorithm are explored to optimize the SVM parameters. The performances of optimized SVM algorithms for temperature extraction are investigated in both simulation and experiment under different conditions for Brillouin gain spectrum collection, resulting in the significant enhancement of sensing accuracy. In particular, the extraction accuracy (i.e., smaller root mean square error value) of temperature information is improved about 4 °C compared with the conventional SVM when the signal-to-noise ratio (SNR) as low as 2.5 dB and 40-ns pump pulse width are adopted in the experiment. In addition to the enhanced accuracy with good robustness, the optimized algorithms have faster processing speed than the curve fitting method, over 20-times improvement. That makes the optimized algorithms become a very promising candidate for high performance BOTDA sensors in the future.