Key Parameter Extraction for Fiber Brillouin Distributed Sensors Based on the Exact Model

Area of Science:

• Optoelectronics
• Optical sensing
• Metrology

Background:

• Accurate temperature and strain measurements are crucial for fiber optic sensing.
• Existing methods for extracting key parameters from Brillouin gain spectra often use simplified models, leading to significant errors.
• Errors in key parameters directly impact the accuracy of temperature and strain measurements.

Purpose of the Study:

• To develop a highly accurate algorithm for extracting key parameters from Brillouin gain spectra.
• To improve the precision of temperature and strain measurements in fiber Brillouin distributed sensors.
• To address the limitations of simplified models in current parameter extraction techniques.

Main Methods:

• Proposed a key parameter extraction algorithm based on the exact Voigt profile.
• Utilized the least-squares method to define an objective function.
• Employed the Levenberg-Marquardt algorithm for objective function minimization and parameter extraction.
• Investigated the impact of Gauss-Hermite quadrature sample points on accuracy and computation time.
• Implemented and compared the proposed algorithm with direct and random algorithms using simulated and experimental data.

Main Results:

• The proposed algorithm demonstrates superior accuracy compared to the direct algorithm, despite the latter's faster computation.
• The random algorithm exhibits an 80-90% convergence rate and is 1-2 times slower with higher fitting errors than the proposed method.
• The proposed algorithm guarantees convergence in all tested cases.
• Analysis provided insights into selecting the optimal number of sample points for Gauss-Hermite quadrature.

Conclusions:

• The proposed algorithm based on the exact Voigt profile significantly enhances the accuracy of key parameter extraction for Brillouin gain spectra.
• This advancement leads to more precise temperature and strain measurements using fiber Brillouin distributed sensors.
• The algorithm offers robust convergence and improved performance over existing methods.