Improving BFS measurement accuracy of BOTDR based on Cauchy proximal splitting

Abstract

A novel approach based on the Cauchy proximal splitting (CPS) algorithm is proposed to improve Brillouin frequency shift (BFS) measurement accuracy in Brillouin optical time domain reflectometry system. The CPS algorithm utilizes proximal splitting to handle the data and the penalty function based on Cauchy distribution to accurately estimate the target signal to promote sparsity, achieving significant improvement in signal-to-noise ratio (SNR). Experimental results confirm that with a SNR increase of 12.7 dB, an increase in BFS measurement accuracy from 4.78 MHz to 0.43 MHz is achieved over a 10.3 km sensing fiber. The denoising effects of the CPS, wavelet denoising (WD) and non-local mean (NLM) algorithms are further compared, demonstrating that the CPS algorithm has the lowest root mean square error (0.43 MHz) and no deterioration in spatial resolution rather than the WD and NLM algorithms does.