RepHARNet: Human Activity Recognition Based on Radar Micro-Doppler Signatures Through Reparameterization

Radar-based human activity recognition (HAR) has emerged as a promising alternative to vision-based systems, as it can operate under poor lighting, occlusions, and privacy-sensitive scenarios. However, existing radar HAR methods often suffer from limited feature extraction capability due to noise, signal attenuation, and the challenge of capturing both global motion patterns and local micro-Doppler dynamics simultaneously. To address these issues, this paper proposes RepHARNet, a novel HAR network built on structural reparameterization for radar micro-Doppler signals. Specifically, RepHARNet decomposes the weight matrices into global and local components through a reparameterization strategy, enabling the network to simultaneously capture coarse-grained inter-channel dependencies via a Global Perceptron and fine-grained intra-channel spatial correlations via a Channel Perceptron. Furthermore, a parameter-efficient share-set mechanism is integrated into the Channel Perceptron to substantially reduce the computational overhead while maintaining the representational capacity. Extensive experiments on the public IMG848 dataset demonstrate that all four RepHARNet variants achieve top-1 accuracies above 93.00%, among which RepHARNet-large achieves the highest at 94.86%, significantly outperforming existing mainstream methods. Additional evaluations on the Ci4R dataset further verify the robustness and effectiveness of RepHARNet, where all variants achieve competitive accuracies above 90.00%. The results verify the effectiveness and superiority of RepHARNet in radar-based HAR.