Through-Wall Human Activity Recognition With Compact MIMO Ultrawideband Radar

Human activity recognition (HAR) in complex scenarios, particularly in through-wall environments, represents a significant frontier in sensing technology research. The implementation of reliable HAR systems faces substantial challenges due to severe noise interference, limited sensor penetration capability, and unpredictable human motion patterns. This work presents two main contributions to address these challenges. First, we develop an integrated HAR system that combines advanced radar signal processing with deep learning approaches. The system features two key innovations: the usage of compact multi-input multi-output (MIMO) ultra-wideband (UWB) radar for capturing multidimensional echo sequences, and a proposed MobRNet architecture that enables efficient feature extraction through integrated radar modules. Second, to systematically evaluate wall obstruction effects on UWB radar data, we establish a comprehensive radar dataset where wall presence serves as the controlled environmental variable with different materials and thickness. The dataset comprises 29,900 radar feature images collected from real indoor environments with various wall materials and thicknesses, accompanied by corresponding video data for validation. Experimental results demonstrate that the proposed system achieves a recognition accuracy of 99% on the total dataset and maintains robust performance with previously unseen subjects, representing an 8% improvement in comprehensive recognition performance compared to state-of-the-art methods.