Cascaded Feedforward Neural Network Based Simultaneously Fast and Precise Multi-Characteristics Extraction and BFS Error Estimation

We present and investigate a scheme to realize fast and precise multi-characteristics extraction and error estimation of Brillouin frequency shift (BFS) simultaneously based on a cascaded feedforward neural network for a Brillouin optical time-domain analyzer (BOTDA) system. The cascaded feedforward neural network is composed of two neural networks in series, where the pre-network is used to extract linewidth (FWHM), signal-to-noise ratio (SNR) and BFS from noised gain spectrum and the post-network utilizes these characteristics and known frequency step to estimate the statistical error of the extracted BFS. The extracted BFS can be used to obtain the environment information along the fiber, while the extracted SNR, FWHM and the estimated BFS error can be utilized to verify the validity of data and monitor the performance of the BOTDA system. The creation process and the performance of the pre-network, post-network and cascaded network are described and analyzed in detail. Both simulation and experimental results show that the cascaded feedforward neural network has higher accuracies on extractions of characteristics (BFS, SNR, FWHM) and error estimation than conventional Lorentzian curve fitting method and error estimation formula when the scanning frequency step of system is large or SNR is low. More importantly, the network is more than 300 times faster than the LCF for simultaneously multi-characteristics extraction and error estimation, which make it possible to real-time monitor the performance of BOTDA system.