基于DB-GS-Yolo11的跨域智能无线感知算法OA

Wireless sensing technology utilizes WiFi signals in the environment to extract feature informa-tion and identify target motion states.With the widespread adoption of smart devices,this technology has been extensively applied in fields such as smart homes,healthcare,human-computer interaction,and au-tonomous driving.However,due to the complex and dynamic nature of mobile communication environ-ments,wireless sensing faces challenges such as low model accuracy,poor scenario generalization,and high environmental dependency.To address these issues across diverse cross-domain scenarios,we pro-pose DB-GS-Yolo11,a dual-branch gated sequential Yolo11-based cross-domain intelligent wireless sensing algorithm.The algorithm employs a dual-branch architecture,integrating Yolo11 neural networks,a Ga-ted Attention Coding(GAC)module,and a State Space Model(SSM).This design enables efficient signal perception and robust extraction of key cross-domain features,significantly improving the model's general-ization capability.The proposed enhancement substantially reduces environmental dependency,endowing the system with greater robustness,portability,and cross-domain recognition accuracy.In comparative experiments,the proposed DB-GS-Yolo11 algorithm is compared in performance with various mainstream neural network models,including Deep Neural Network(DNN),Gated Recurrent Unit(GRU),and Google Inception Net neural network(GoogLeNet).The experimental results show that DB-GS-Yolo11 exhibits superior performance in optimizing perceptual complexity,improving recognition speed,and cross domain adaptability.The overall perception accuracy in the domain dataset has improved by 5.33%to 9.67%,and the recognition efficiency has increased by 1.35%to 17.81%.The recognition accuracy of the algorithm proposed on cross domain datasets such as cross position and cross direction has been improved by 2.33%to 7.33%and 2.67%to 4.33%while the recognition efficiency has been improved by 1.63%to 4.69%and 0.23%to 3.11%respectively.