Deep Learning Assisted Microwave Photonic Dual-Parameter Sensing

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.