Electronics Letters (Jan 2023)

Performance enhancement of arrayed waveguide grating‐based fibre Bragg grating interrogation assisted by random forest

  • Zizheng Yue,
  • Wenbo Li,
  • Di Zheng,
  • Changjian Xie,
  • Wei Pan,
  • Xihua Zou

DOI
https://doi.org/10.1049/ell2.12682
Journal volume & issue
Vol. 59, no. 1
pp. n/a – n/a

Abstract

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Abstract The random forest, a powerful machine learning algorithm, is introduced to improve the performance of silicon arrayed waveguide grating (AWG)‐based fibre Bragg grating (FBG) wavelength interrogation. The experimental results show the proposed method has high interrogation accuracy with the root mean squared error (RMSE) of 0.73 pm in the whole demodulation range.

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