Deep Learning Assisted Microwave Photonic Dual-Parameter Sensing
Xiaoyi Tian, Luping Zhou, Liwei Li, Giorgio Gunawan, Linh Nguyen, Xiaoke Yi
Abstract
The combination of optical microresonators and the emerging microwave photonic (MWP) sensing has recently drawn great attention, whereas its multi-parameter sensing capability mainly relies on adopting multiple resonance modes. By incorporating deep learning (DL) into MWP sensing, we propose a new sensing paradigm, which has the simplified design, reduced fabrication requirement, and the capability of sensing more than one parameter. The MWP interrogation transforms the spectral response of a single optical resonance (SOR) that can be at arbitrary coupling conditions into the variations of the zero-transmission profile of microwave signals, providing improved interrogation resolution regardless of the resonance parameters. A DL unit is used to exploit the raw interrogation output to simultaneously estimate the target measurands. As the proof-of-concept demonstration, simultaneous temperature and humidity sensing using a SOR is conducted, where the convolutional neural tangent kernel (CNTK) is used as the DL model to reduce the demand for experimental data. The established CNTK-DL model consistently outperforms the support vector regression model that relies on handcrafted features and demonstrates an over 2-fold higher estimation accuracy with the laser drift interference and a lower mean absolute error in the presence of strong noise, showing the power of DL for boosting MWP sensing.