Experimental Microwave Imaging System Calibration via Cycle-GAN

Microwave (or electromagnetic) imaging systems seek a quantitative reconstruction of the target permittivity. Such systems require calibration of the raw
$S$
-parameter data. This calibration is especially difficult in some systems where known targets cannot be introduced (e.g., grain bin imaging). Herein we present a machine-learning-based method of calibrating such systems that does not require measuring a known target inside of the imaging chamber. Using a cycle-generative adversarial network (Cycle-GAN) machine-learning network, we show that we can calibrate data from a 2-D scalar microwave imaging (MWI) system and successfully reconstruct targets. Cycle-GAN makes use of two sets of data: experimental and synthetic. Unlike traditional calibration, there is no need for the experimental data to be labeled, i.e., the calibration targets do not need to be “known.” The results show that with an experimental calibration set of roughly 150 targets, the Cycle-GAN approach provides comparable results to known-target calibration for the class of targets we used in this 2-D near-field MWI system.