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Through-wall radars are researched and developed for the detection,localization, and tracking of human activities in indoor environments.Electromagnetic wave propagation through walls introduces refraction,attenuation, multipath, and ghost targets in the radar signatures. Estimationof wall characteristics (dielectric profile and thickness) can enable walleffects to be deconvolved from through-wall radar signatures. We propose to usegenerative adversarial networks (GAN) to estimate wall characteristics fromnarrowband scattered electric fields on the same side of the wall as thetransmitter. We demonstrate that the GANs, consisting of two neural networksconfigured in an adversarial manner, are capable of solving the highlynonlinear regression problem with limited training data to estimate thedielectric profile and thickness of actual walls up to 95\% accuracy based ontraining with simulated data generated from full-wave solvers.

Estimation of Electrical Characteristics of Inhomogeneous Walls Using Generative Adversarial Networks