  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. Estimation
of wall characteristics (dielectric profile and thickness) can enable wall
effects to be deconvolved from through-wall radar signatures. We propose to use
generative adversarial networks (GAN) to estimate wall characteristics from
narrowband scattered electric fields on the same side of the wall as the
transmitter. We demonstrate that the GANs, consisting of two neural networks
configured in an adversarial manner, are capable of solving the highly
nonlinear regression problem with limited training data to estimate the
dielectric profile and thickness of actual walls up to 95\% accuracy based on
training with simulated data generated from full-wave solvers.
