High-Quality Synthetic Data Generation for Omnidirectional Human Activity Recognition

The impact of aspect angle on Doppler effect hinders the capability of a monostatic radar to achieve human activity recognition (HAR) from all aspect angles, i.e., omnidirectional. To alleviate the 'angle sensitivity', sufficient and high-quality training data from multiple aspect angles is mandated. However, it would be time-consuming for the monostatic radar to collect the training data from all aspect angles. To address this issue, this paper proposes a high-quality synthetic data generation algorithm based on high-dimensional model representation (HDMR) for omnidirectional HAR. The aim is to augment a high-quality dataset with collected samples at the radar line-of-sight direction and few samples from other aspect angles. The quality of synthetic samples is evaluated by dynamic time wrapping distance (DTWD) between the synthetic and real samples. Subsequently, the synthetic samples are utilized to train a classifier based on ResNet50 to achieve omnidirectional HAR. Experimental results demonstrate that the averaged HAR accuracy of the proposed algorithm exceeds 91% at different aspect angles. The quality of the synthetic samples generated by the proposed algorithm outperforms two commonly-used algorithms in the literature.