Model-Based Position and Reflectivity Estimation of Fiber Bragg Grating Sensor Arrays

Area of Science:

• Photonics and Optical Sensing
• Signal Processing
• Fiber Optic Technology

Background:

• Fiber Bragg Grating (FBG) arrays are crucial for distributed sensing applications.
• Conventional signal processing methods face limitations in accuracy and efficiency, especially near spatial resolution limits.
• Existing techniques struggle with systematic errors like crosstalk and spectral shadowing in large FBG systems.

Purpose of the Study:

• To propose an efficient model-based signal processing approach for FBG arrays.
• To detail position and reflectivity estimation algorithms using Estimation of Distribution Algorithms (EDA) and Transfer Matrix Models (TMM).
• To evaluate the performance of the proposed methods against conventional techniques using simulations and experimental data.

Main Methods:

• Development of a model-based signal processing framework for FBG sensing.
• Implementation of an Estimation of Distribution Algorithm (EDA) for position estimation.
• Application of a parametric Transfer Matrix Model (TMM) for reflectivity estimation.
• Validation using Monte Carlo simulations and incoherent optical frequency domain reflectometry (iOFDR) data.

Main Results:

• The model-based approach significantly outperforms traditional Fourier transform processing.
• The proposed method demonstrates superior performance near the spatial resolution limit.
• Electrical bandwidth and measurement time are substantially reduced.
• The TMM approach offers flexibility for complex topologies and incorporation of prior sensor knowledge.

Conclusions:

• The developed model-based signal processing approach offers an efficient and accurate solution for FBG sensing.
• The method shows promise for improving the performance of large-scale FBG sensor systems by addressing systematic errors.
• The flexible model-based framework can be adapted for various sensing configurations and requirements.