  A major obstacle in radar based methods for concealed object detection on
humans and seamless integration into security and access control system is the
difficulty in collecting high quality radar signal data. Generative adversarial
networks (GAN) have shown promise in data generation application in the fields
of image and audio processing. As such, this paper proposes the design of a GAN
for application in radar signal generation. Data collected using the
Finite-Difference Time-Domain (FDTD) method on three concealed object classes
(no object, large object, and small object) were used as training data to train
a GAN to generate radar signal samples for each class. The proposed GAN
generated radar signal data which was indistinguishable from the training data
by qualitative human observers.
