Abstract
Brillouin scattering-based distributed fiber optic sensor technology is an interesting technique for measuring temperature and strain in distributed manner. This technique has found its applications ranging from the structural health monitoring application of civil structures to the biomedical imaging applications. In general, Brillouin based sensor exploits two types of Brillouin scatterings, which are the spontaneous Brillouin scattering (SpBS) and the stimulated Brillouin scattering (SBS). SBS is used in Brillouin optical time domain (BOTDA) sensor for strain/temperature measurement. This paper mainly explains the recent developments of Brillouin optical time domain reflectometry (BOTDR) sensor that utilizes SpBS for distributed strain and temperature measurement, for improving its sensing performance such as signal-to-noise ratio (SNR), measurement time, spatial resolution, and sensing range. Besides, the recent application of machine learning (ML) in improving the sensing performance of BOTDA has also been reported and will also be highlighted.
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