Abstract:With wide applications of Brillouin optical fiber time domain analysis (BOTDA) sensing technology in safety monitoring of many large-scale infrastructure projects, much more stricter requirements are put on measurement accuracy and real-time. If the Brillouin frequency shift is extracted with the traditional least squares curve fitting, the accuracy of the measurement results depends mainly on the selection of the initial value and the influence of noise. And the iterative solution process of fitting algorithm increases the data processing time, which is not conducive to engineering real-time. In this paper, for estimating the central frequency of noisy Lorentzian curves acquired from the measurements with BOTDA sensors, it is summarized the curve fitting algorithms based on optimal estimation of nonlinear parameters and the hybrid fitting algorithms based on the neural network for feature extraction of Brillouin scattering spectrum. In addition, the cross correlation method(XCM), deep-learning(DL) method and the centroid detection algorithm(CDA) are introduced.
