Selection of spectrum model in estimation of Brillouin frequency shift for distributed optical fiber sensor

The original Lorentzian profile-based nonlinear least-squares problem for BFS estimation is converted to a linear least-squares one. Then, Brillouin frequency shift (BFS) can be readily calculated. The typical nonlinear least-squares Lorentzian, Gaussian, pseudo-Voigt and Voigt fits using Levenberg-Marquardt algorithm, the correlation-based algorithm and the proposed Lorentzian model-based linear least-squares fitting algorithm are used to estimate BFS of the measured Brillouin spectra with different values of SNR (signal-to-noise ratio) and frequency step. The results reveal that the proposed algorithm has similar accuracy with the nonlinear fits. However, the computation time of the typical nonlinear fits is 187.56–11481.67 times as long as the proposed algorithm. The proposed algorithm can estimate BFS with less computational burden and higher accuracy than the correlation-based algorithm. If SNR is low, the frequency step should be set to a low value.

Accurate and ultra-fast estimation of Brillouin frequency shift for distributed fiber sensors

Distributed optical fiber Brillouin sensors can monitor the temperature and strain along a fiber by estimating the Brillouin frequency shift (BFS) according to the measured Brillouin spectrum. The system performance is highly dependent on the algorithm of BFS extraction. The well-established Lorentz curve fitting (LCF) method is generally employed because the Brillouin spectrum theoretically satisfies a Lorentz shape. Recently, machine-learning methods have been proposed for more effective BFS extraction, but they have some drawbacks and limitations. The machine-learning algorithms require a large amount of data and high computing power to find suitable extraction methods. However, with prior knowledge, Brillouin spectrum can be treated as a regular signal that requires only three degrees of freedom to define. The unique sparsity characteristics of Brillouin spectrum have not been well studied or exploited. In this paper, we propose a sparse representation method for Brillouin spectrum that extracts three sparse features of the Brillouin spectrum through the dictionary-learning algorithm (K-means singular value decomposition). The correlation between the sparse coefficient and the BFS is experimentally calibrated and verified. The accuracy of the proposed algorithm is comparable to that of LCF, and its processing is six times faster. This sparse representation method for Brillouin spectra is promising as an alternative universal BFS extraction method for distributed Brillouin sensors.

Sparse representation of Brillouin spectrum using dictionary learning.

Brillouin scattering-based distributed optical fiber sensors have been successfully employed in various applications in recent decades, because of benefits such as small size, light weight, electromagnetic immunity, and continuous monitoring of temperature and strain. However, the data processing requirements for the Brillouin Gain Spectrum (BGS) restrict further improvement of monitoring performance and limit the application of real-time measurements. Studies using Feedforward Neural Network (FNN) to measure Brillouin Frequency Shift (BFS) have been performed in recent years to validate the possibility of improving measurement performance. In this work, a novel FNN that is 3 times faster than previous FNNs is proposed to improve BFS measurement performance. More specifically, after the original Brillouin Gain Spectrum (BGS) is preprocessed by Principal Component Analysis (PCA), the data are fed into the Feedforward Neural Network (FNN) to predict BFS.

Fast Measurement of Brillouin Frequency Shift in Optical Fiber Based on a Novel Feedforward Neural Network

A superfast Brillouin frequency shift estimation method based on the mixed moment for the distributed optical fiber sensor

A fast interpolation algorithm based on cross-correlation is proposed to estimate the BFS in BOTDA systems The performances of the proposal are investigated through simulation and experiment in this paper The simulation results demonstrate that the computational cost of the proposed algorithm is reduced to less than 1/22, 1/36, 1/2 and 1/5 of the Lorentzian fitting, upsampling cross-correlation, partial quadratic fitting and partial cross-correlation algorithm respectively The experimental results indicate that the error of BFS extracted by our proposal is the same as that of the other schemes when no hotspot is presented and the error of BFS is smaller in cross-correlation based schemes than that in Lorentzian fitting scheme around hotspot

xingyun zhang

Papers

A fast method for Brillouin frequency shift estimation

Data for: A fast method for Brillouin frequency shift estimation