Abstract

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 sparsely sampled frequency response, whilst the interrogation speed is greatly improved. More importantly, the well-trained GPR model remains valid for the filter frequency response with large notch depth fluctuation, greatly increase the tolerance to device parameter deviation and ambient changes. This work demonstrates that the machine learning algorithms will provide a new avenue for microwave photonics filtering interrogation with the improved performance.

High speed and high resolution interrogation of a fiber Bragg grating sensor based on microwave photonic filtering and chirped microwave pulse compression

Ou Xu, Jiejun Zhang, and Jianping Yao
Opt. Lett. 41(21) 4859-4862 (2016)

High-resolution fiber Bragg grating based transverse load sensor using microwave photonics filtering technique

Yiping Wang, Ming Wang, Wei Xia, and Xiaoqi Ni
Opt. Express 24(16) 17960-17967 (2016)

Long fiber Bragg grating sensor interrogation using discrete-time microwave photonic filtering techniques

Amelia Lavinia Ricchiuti, David Barrera, Salvador Sales, Luc Thevenaz, and José Capmany
Opt. Express 21(23) 28175-28181 (2013)

Ultra-short FBG based distributed sensing using shifted optical Gaussian filters and microwave-network analysis

Rui Cheng, Li Xia, Chaotan Sima, Yanli Ran, Jalal Rohollahnejad, Jiaao Zhou, Yongqiang Wen, and Can Yu
Opt. Express 24(3) 2466-2484 (2016)

Resolution-improved in situ DNA hybridization detection based on microwave photonic interrogation

Yuan Cao, Tuan Guo, Xudong Wang, Dandan Sun, Yang Ran, Xinhuan Feng, and Bai-ou Guan
Opt. Express 23(21) 27061-27070 (2015)