Research on Cross-Scene Human Activity Recognition Based on Radar and Wi-Fi Multimodal Fusion

Radar-based human behavior recognition has significant value in IoT application scenarios such as smart healthcare and intelligent security. However, the existing unimodal perception architecture is susceptible to multipath effects, which can lead to feature drift, and the issue of limited cross-scenario generalization ability has not been effectively addressed. Although Wi-Fi sensing technology has emerged as a promising research direction due to its widespread device applicability and privacy protection, its drawbacks, such as low signal resolution and weak anti-interference ability, limit behavior recognition accuracy. To address these challenges, this paper proposes a dynamic adaptive behavior recognition method based on the complementary fusion of radar and Wi-Fi signals. By constructing a cross-modal spatiotemporal feature alignment module, the method achieves heterogeneous signal representation space mapping. A dynamic weight allocation strategy guided by attention is adopted to effectively suppress environmental interference and improve feature discriminability. Experimental results show that, on a cross-environment behavior dataset, the proposed method achieves an average recognition accuracy of 94.8%, which is a significant improvement compared to the radar unimodal domain adaptation method.