Robust Signal Denoising Techniques for Highly Accurate and Spatial Resolution Preserved Brillouin Optical Time Domain Analysis Sensors

Abstract

The diminution of measurement uncertainty to an acceptable level and the preservation of experimental spatial resolution are highly desirable in many real-world applications of Brillouin optical time domain analysis (BOTDA) sensors. In practical applications of such sensors, the measurement uncertainty essentially relies upon the signal-to-noise ratio (SNR) of the experimental Brillouin gain spectra (BGSs) obtained throughout the sensing fiber. The improvement of such SNR using improper signal denoising techniques alters the experimental spatial resolution of the BOTDA sensors. In this paper, the use of non-local means filter (NLMF) and anisotropic diffusion filter (ADF) is experimentally demonstrated to enhance the uncertainty in the measurement of temperature using BOTDA sensors. For this purpose, the BGSs along a 41 km fiber are collected by averaging several numbers of BOTDA-traces from BOTDA hardware setup. Such BGSs are first denoised by employing NLMF and ADF to improve the measurement SNR. The Brillouin frequency shifts (BFSs) of denoised BGSs are then extracted via curve fitting technique (CFT). The BFS distribution is finally mapped to temperature distribution depending on the known BFS-temperature relationship of the sensing fiber. The robustness of the used filters is analyzed rigorously in terms of SNRs of BGSs, uncertainty in temperature extraction, spatial resolution and runtime in signal processing. The results indicate that the utilization of NLMF and ADF can enhance the SNRs of BGSs up to the maximum of 15.64 dB and 13.53 dB, respectively. Consequently, the uncertainties in the temperature extraction can be reduced up to the maximum of 52.63% and 57.31% for using NLMF and ADF, respectively. Moreover, both NLMF and ADF can preserve the experimental spatial resolution of the BOTDA sensors and include insignificant runtime to CFT. Thus, NLMF and ADF can be considered as robust signal denoising techniques for highly accurate and spatial resolution preserved BOTDA sensors.

DUJASE Vol. 7 (2) 31-38, 2022 (July)