Gaussian process applied in temperature compensation for FBG strain sensing

This paper presents a novel approach using Gaussian Process (GP) for temperature compensation of fiber Bragg grating (FBG) strain sensing. Firstly the basic theory of GP regress is demonstrated. Then the training samples are employed to train Gaussian Process, and the testing samples are applied to detect the predict ability of the method. Error analysis shows that Gaussian Process is effective in restraining the temperature disturbance to strain sensing of FBG.