Data-Driven Complementary Power Measurement for Microwave Instantaneous Frequency Estimation

A high-precision photonic based instantaneous frequency measurement system driven by machine learning is proposed and experimentally demonstrated. A three-layer deep neural network is used to tackle device nonlinearity and system noise, resulting in absolute error <50 MHz and 1.1 MHz root mean square error.