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Deep neural networks (DNNs) have recently received vast attention inapplications requiring classification of radar returns, including radar-basedhuman activity recognition for security, smart homes, assisted living, andbiomedicine. However,acquiring a sufficiently large training dataset remains adaunting task due to the high human costs and resources required for radar datacollection. In this paper, an extended approach to adversarial learning isproposed for generation of synthetic radar micro-Doppler signatures that arewell-adapted to different environments. The synthetic data is evaluated usingvisual interpretation, analysis of kinematic consistency, data diversity,dimensions of the latent space, and saliency maps. A principle-componentanalysis (PCA) based kinematic-sifting algorithm is introduced to ensure thatsynthetic signatures are consistent with physically possible human motions. Thesynthetic dataset is used to train a 19-layer deep convolutional neural network(DCNN) to classify micro-Doppler signatures acquired from an environmentdifferent from that of the dataset supplied to the adversarial network. Anoverall accuracy 93% is achieved on a dataset that contains multiple aspectangles (0 deg., 30 deg., and 45 deg. as well as 60 deg.), with 9% improvementas a result of kinematic sifting.

Motion Classification using Kinematically Sifted ACGAN-Synthesized Radar Micro-Doppler Signatures