Omnidirectional Spectrogram Generation for Radar-Based Omnidirectional Human Activity Recognition

Micro-Doppler-based human activity recognition (HAR) has been extensively researched in remote sensing. However, a well-performing classifier requires sufficient radar data of omnidirectional human movements due to the "angle sensitivity," resulting in high costs for radar data acquisition. To address this issue, this study defines for the first time the task of "omnidirectional spectrogram generation" and proposes a method to obtain enough omnidirectional spectrograms based on the spectrograms of human movements in one direction. It significantly reduces the dependence on radar measurements with omnidirectional setups. Our method is founded upon an image translation framework that is enhanced by incorporating the concept of information disentanglement and a proposed feature-level unbiased domain translation strategy. They enable us to generate high-quality omnidirectional spectrograms at various aspect angles. The generated spectrograms are then used as training support of omnidirectional micro-Doppler-based classifiers. Subsequently, we conduct an in-depth analysis of the metric correlation between the quality of generated spectrograms and the performance of these classifiers. Finally, we introduce a method for evaluating this correlation by proposed criterion. Our method is evaluated based on a radar simulation dataset, and the results show that it significantly exceeds the compared methods, demonstrating its great potential for the task of omnidirectional recognition of human activities. Besides, we find that there is a significant correlation between classification accuracy and several image quality assessment metrics, and we believe that this investigation will serve as the foundation for future research on assessing the contribution of data generation methods to downstream tasks using quantitative measures.