基于经验模态分解的单端BOTDA系统降噪方法研究

Denoising Method for Single-Ended BOTDA System Based on Empirical Mode Decomposition

针对单端布里渊光时域分析(BOTDA)系统存在噪声大、信噪比较低等不足,提出一种基于经验模态分解的降噪方法。理论分析经验模态分解的降噪原理和少模光纤单端布里渊光时域分析传感原理,通过搭建的单端结构布里渊光时域分析温度传感系统,对经验模态分解的降噪效果进行对比分析。实验和仿真结果表明,经验模态分解算法对温度传感系统具有良好的降噪效果,降噪后信噪比提升了约3.06dB,温度测量精度提升了约0.98℃。

To address the problems of high noise and low signal-to-noise ratio in single-ended Brillouin optical time-domain analysis systems,a denoising method based on empirical mode decomposition(EMD)was developed.Theoretical analyses of the denoising principle of EMD and the sensing principle of single-ended Brillouin optical time-domain analysis in a few-mode optical fiber were conducted.The denoising effect of EMD was comparatively analyzed by constructing a single-ended Brillouin optical time-domain analysis temperature-sensing system.The experimental and simulation results show that the EMD algorithm exhibited a good denoising effect on the temperature sensing system and that it improved the signal-to-noise ratio and temperature measurement accuracy by approximately 3.06dB and 0.98℃,respectively.