  Narrowband radar micro-Doppler signatures are heavily used to identify and
classify human activities. When the radar is operated in through-wall
environments, the complex electromagnetic propagation phenomenology introduces
considerable distortions in the micro-Doppler signatures through attenuation
and multipath. The problem is particularly severe in inhomogeneous wall
scenarios involving multiple wall layers, air gaps, or metal reinforcements.
Through-wall radar data collection using simulations and measurements involves
significant time and effort. In this paper, we propose an alternative method of
synthesizing through-wall radar micro-Doppler signatures from their free space
counterparts using the generative adversarial network (GAN). We train the GAN
using radar micro-Doppler signatures generated from electromagnetic
simulations. We generate the radar data for different human motions, along
different orientations, and under diverse through-wall conditions. The
synthetic radar micro-Dopplers generated from the neural networks are then
evaluated for their realism using a denoising autoencoder, which shows an
excellent realism score.
