Performance Upgradation of Microwave Photonic Filtering Interrogation Using Gaussian Process Regression

The Gaussian process regression (GPR), a powerful machine learning tool, is introduced to upgrade the microwave photonic filtering interrogation, with improved demodulation speed and accuracy. In a fiber Bragg grating (FBG) based microwave photonic filtering interrogation system for strain sensing, the GPR is employed to learn the relationship between the frequency response of microwave photonic filter and the strain applied on the sensing FBG. Compared with the traditional direct-notch-detection method, the proposed method can achieve better measurement accuracy under the condition of sparse sampling, whilst the interrogation speed is greatly improved. More importantly, the proposed interrogation technique exhibits strong robustness to the variation of notch depth, which greatly relaxes the requirement of the weight balance between two taps and environmental stability. This work demonstrates that the machine learning algorithms will provide a new avenue for microwave photonics filtering interrogation with the improved performance.