Computational Compressed Sensing of Fiber Bragg Gratings

TL;DR

This paper introduces a computational compressed sensing approach for fiber Bragg grating interrogation, significantly reducing system complexity and enabling effective temperature sensing and sensor network interrogation with a simplified setup.

Contribution

It presents a novel application of compressed sensing techniques to fiber Bragg grating interrogation, reducing hardware complexity and enabling new strategies for sensor network analysis.

Findings

Achieved temperature sensing with accuracy comparable to traditional methods.

Demonstrated effective interrogation of sensor networks using CS techniques.

Proved the feasibility of low-complexity, small-footprint interrogator configurations.

Abstract

State-of-the-art fiber Bragg grating interrogators utilize mature concepts and technologies like tunable lasers, optical spectrum analyzers and a combination of time, wavelength, or spatial division demultiplexing approaches. Here, we propose the use of computational compressed sensing (CS) techniques for interrogation of fiber Bragg gratings, reducing interrogator complexity by using a broadband ASE light source and single-pixel detection. We demonstrate temperature sensing using a pre-calibration approach to achieve reconstruction accuracy comparable to uncompressed measurements. We extend these principles for the interrogation of sensor networks, presenting strategies for tackling sparsity considerations. Our proof-of-principle demonstrations show how the presented computational compressed sensing techniques can provide an alternative for realizing low-complexity, small footprint interrogator configurations.