A Dual-Adaptive Denoising Algorithm for Brillouin Optical Time Domain Analysis Sensor

Abstract

A dual-adaptive denoising algorithm based on complementary ensemble empirical mode decomposition (CEEMD) and particle swarm optimization (PSO) is proposed to improve the signal-to-noise ratio (SNR) of Brillouin optical time domain analysis (BOTDA) sensor. Here, CEEMD is employed to decompose the measurement signal into a series of intrinsic mode functions (IMFs) adaptively and accurately. The spectrum centroid method is applied to quantify objectively the range of denoising. Then PSO threshold algorithm is dedicatedly designed to search for the optimal/sub-optimal denoising threshold for noised IMFs, which can adjust the threshold size adaptively according to the noise level of input signal. The results show that the SNR can be improved by more than 14 dB and the Brillouin frequency shift (BFS) accuracy is optimized by 1.12 MHz in 22.5 km sensing fiber. This algorithm does not need any condition prediction in the process of denoising, which offers an adaptation and high SNR demodulation scheme for BOTDA sensor with only modifying the software.