BOTDA Signal Extraction Technology Based On Machine Learning Algorithm

With the rapid development of the optical communication industry,optical fiber sensor has become an important part of the structural health detection field.Meanwhile,it is widely used in various fields of national economy and national defense.At present,distributed optical fiber sensor gets a great development.However,for distributed fiber-optic sensing information with 'exponential growth',the traditional Lorentz fitting method is difficult to satisfy the requirements of the systems for real-time sensing information extraction.How to obtain distributed optical fiber sensing information efficiently and accurately is a hot topic of current research.Based on the analysis and summary of the distributed optical fiber sensing technology,this paper takes the Brillouin distributed optical fiber sensing system as an example to discuss the key technologies in the information extraction process.Furthermore,the method for extracting Brillouin sensor information using Sobel edge detection operator and BP neural network is proposed.The specific work and innovations are as follows:(1)Firstly,the paper analyzes the problems existing in the process of information extraction by spectral subtraction.Then,the optimization and improvement are carried out based on the spectral subtraction method.Beyond that,the algorithm of extracting Brillouin sensor information by Sobel edge detection operator is put forward.The method not only considers the Brillouin spectrum as an image but also extracts the effective information by Sobel edge detection algorithm to obtain the frequency shifted section(s).As shown by the experimental results,compared with the traditional Lorentz fitting method,the time complexity of the algorithm in this study is about 1/9 and the accuracy of determining the frequency shift section(s)is also improved by 27.9%.(2)This paper further improves the above algorithm and proposes an algorithm of extracting Brillouin sensor information based on BP neural network.Furthermore,it uses the characteristics of BP neural network that can realize any complex nonlinear mapping to determine whether frequency shift occurs and thereby quickly obtains sensing information in Brillouin fiber optic sensors.The number of training functions,transfer functions,and hidden layer nodes in the BP neural network are determined by the control variable method.As indicated by the experimental results,compared with the traditional Lorentz fitting method,the time complexity of the algorithm in this study is about 1/12 and the accuracy of determining the frequency shift section(s)is also improved by 79.4%.