Computational Compressed Sensing of Fiber Bragg Gratings
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.