Hybrid optimization algorithm of Brillouin scattering spectra fitting

In distributed fiber optic sensing system based on stimulated Brillouin scattering optical time domain analysis ,the information in temperature or strain measurements is difficult to identify because of the noise mixed into the probe signal. The accuracy of spectral fitting is very important for the identification of sensor information. For the case of the low SNR sensing system, this paper proposes a fitting method to extract high accuracy Brillouin scattering spectral features, which uses wavelet analysis combining the BP (back propagation) network of Levenberg-Marquardt(LM) algorithm to adjust the connection weights. This method overcomes the shortcoming of BP neural networks easy falling into local minima. Meanwhile, the method ensures the precision of values. Numerical simulations show that the method is suitable for different weight ratios, different spectral line widths, low signal to noise ratio and spectral fitting in large scope. With the SNR of 10 dB, the degrees of fitting obtained are more than 0.96. In addition, experimental results demonstrate that this method is suitable for the extraction of the feature of Brillouin scattering spectrum under the circumstances of multiple pump power extraction, and it has higher precision than traditional BP neural network algorithm.