Synthesis of Micro-Doppler Signatures of Human Activities from Different Aspect Angles Using Generative Adversarial Networks

In this paper, we propose to produce synthesized micro-Doppler signatures from different aspect angles through conditional generative adversarial networks (cGANs). Micro-Doppler signatures of non-rigid human body motions vary considerably as a function of the radar's aspect angle. Because the direction of the human motion can be arbitrary, a large volume of training data across diverse aspects is needed for practical target activity classification through machine learning. As measurements can require significant monetary and labor costs, the synthesis of micro-Doppler signatures can be an alternate solution. Therefore, we investigate the feasibility of data augmentation through synthesizing micro-Doppler signatures of human activities from diverse radar aspect angles with input data from a single aspect angle. For the training data, the micro-Doppler radar signatures of 12 human activities are generated from different angles ranging from 0 to 315 degrees, at 45-degree increments, through simulations. For each angle, cGANs are trained to synthesize the micro-Doppler signatures for that specific angle given micro-Doppler signatures from another angle. The output of each model is evaluated by calculating mean-square errors and structural similarity indexes between the synthesized micro-Doppler signatures and the ground-truth ones obtained from simulations. We test three different scenarios, and report the respective results.