  Recently, deep neural networks (DNNs) have been the subject of intense
research for the classification of radio frequency (RF) signals, such as
synthetic aperture radar (SAR) imagery or micro-Doppler signatures. However, a
fundamental challenge is the typically small amount of data available due to
the high costs and resources required for measurements. Small datasets limit
the depth of DNNs implementable, and limit performance. In this work, a novel
method for generating diversified radar micro-Doppler signatures using
Kinect-based motion capture simulations is proposed as a training database for
transfer learning with DNNs. In particular, it is shown that together with
residual learning, the proposed DivNet approach allows for the construction of
deeper neural networks and offers improved performance in comparison to
transfer learning from optical imagery. Furthermore, it is shown that
initializing the network using diversified synthetic micro-Doppler signatures
enables not only robust performance for previously unseen target profiles, but
also class generalization. Results are presented for 7-class and 11-class human
activity recognition scenarios using a 4-GHz continuous wave (CW)
software-defined radar.
