  Deep neural networks (DNNs) have recently received vast attention in
applications requiring classification of radar returns, including radar-based
human activity recognition for security, smart homes, assisted living, and
biomedicine. However,acquiring a sufficiently large training dataset remains a
daunting task due to the high human costs and resources required for radar data
collection. In this paper, an extended approach to adversarial learning is
proposed for generation of synthetic radar micro-Doppler signatures that are
well-adapted to different environments. The synthetic data is evaluated using
visual interpretation, analysis of kinematic consistency, data diversity,
dimensions of the latent space, and saliency maps. A principle-component
analysis (PCA) based kinematic-sifting algorithm is introduced to ensure that
synthetic signatures are consistent with physically possible human motions. The
synthetic dataset is used to train a 19-layer deep convolutional neural network
(DCNN) to classify micro-Doppler signatures acquired from an environment
different from that of the dataset supplied to the adversarial network. An
overall accuracy 93% is achieved on a dataset that contains multiple aspect
angles (0 deg., 30 deg., and 45 deg. as well as 60 deg.), with 9% improvement
as a result of kinematic sifting.
