An intelligent wireless sensing algorithm for complex cross-domain scenarios based on DB-FA-YoLov6

Wireless sensing technology can identify human motion via feature information from WiFi signals. The popularity of smartphones, wearable devices, and other smart devices has increased the use of wireless sensing in fields such as smart homes, smart healthcare, human–computer interaction, and autonomous vehicles. However, the mobile communication environment is complex and dynamic which makes wireless sensing challenging. The issues include low model sensing accuracy, poor scene generalization ability, and high environmental dependence. Therefore, this paper proposes a cross-domain intelligent wireless sensing algorithm based on a double branch frequency attention mechanism Yolov6 network called DB-FA-YoLov6. This integrates a Yolov6 neural network, frequency attention module, and residual module to provide efficient extraction of signal features and enhance model generalization. The goal is to reduce the effect of the environment on sensing tasks and improve robustness, portability, and cross-domain accuracy. The DB-FA-YOLOv6 model integrates two types of residual modules, BasicBlock and Bottleneck. It replaces the large modules in the Yolov6 network model with lightweight structures, which can decrease the number of parameters, improve the efficiency of model training and testing, and reduce the complexity. Compared with current sensing algorithms such as Vision Transformer Network for Multiple Vision Tasks (ViT-MVT), Environment Independent (EI), and Joint Adversarial Domain Adaptation (JADA), the proposed DB-FA-YOLOv6 algorithm has better sensing accuracy, sensing efficiency, and cross-domain performance. For the in-domain scenario, the proposed algorithm achieves improvements of 10.0 % in sensing accuracy and 10.1 % in sensing efficiency. The sensing accuracy of the proposed algorithm in cross-domain scenarios, namely location and orientation, is improved by 10.5 % and 9.7 %, and the sensing efficiency is improved by 7.0 % and 52.1 %, respectively.