Physics-Aware Design of Multi-Branch GAN for Human RF Micro-Doppler Signature Synthesis

Generative adversarial networks (GANs) have been recently proposed for the synthesis of RF micro-Doppler signatures to mitigate the problem of low sample support and enable the training of deeper neural networks (DNNs) for improved RF signal classification. However, when applied to human micro-Doppler signatures for gait analysis, GANs suffer from systemic kinematic discrepancies that degrade performance. As a solution to this problem, this paper proposes the design of a physics-aware loss function and multi-branch GAN architecture. Our results show that RF gait signatures synthesized using the proposed approached have greater correlation and similarity to measured RF gait signatures, while also improving the accuracy in classifying five different gaits.