Machine Learning Approach to Data Processing of TFBG-Assisted SPR Sensors

• Published in: Journal of lightwave technology 2022-05, Vol.40 (9), p.3046-3054
• Main Authors: Chubchev, Eugeny, Tomyshev, Kirill, Nechepurenko, Igor, Dorofeenko, Alexander, Butov, Oleg
• Format: Article
• Language: English
• Subjects: Bragg gratings; Data processing; Environmental monitoring; Fiber optics; Machine learning; machine learning algorithm; Machine learning algorithms; optical fiber devices; Optical fiber sensors; Optical fibers; Plasmons; Refractive index; Refractivity; Remote monitoring; Remote sensing; Remote sensors; Sensors; Temperature sensors
• ISSN: 0733-8724; 1558-2213
• DOI: 10.1109/JLT.2022.3148533

Fiber optic sensors are applied in industry, remote sensing, environmental monitoring and healthcare. A special place is occupied by tilted fiber Bragg gratings, which can significantly expand the capabilities provided by standard Bragg sensors. But these gratings have complex spectral responses, therefore, data processing becomes a critical task for achieving maximum performance. In this paper, machine learning methods for processing spectral data of a plasmonic fiber sensor based on a tilted fiber Bragg grating were applied for the first time for the measurement of small refractive index changes. The responses of two similar but not identical sensors were measured in two independent experiments. The model trained on the data of the first sensor was used to analyze data obtained with another sensor. The best resolution achieved in our experiments was 9 \times {10^{ - 6}} RIU.