Papers
1
Total Citations
13
H-Index
1
About
Shuai Xue is a researcher advancing the field of indoor localization and navigation, with a focus on leveraging ubiquitous wireless infrastructure. His key research areas include Wi-Fi-based positioning systems, deep learning for spatial intelligence, and robot-aided sensing. His most notable contribution, "Wi-Fi-Based Indoor Localization and Navigation: A Robot-Aided Hybrid Deep Learning Approach" (2023, 13 citations), introduces a novel hybrid deep learning framework that combines robot-assisted data collection with sophisticated neural network architectures. This work addresses the critical challenge of achieving high-precision indoor positioning without requiring extensive manual calibration or specialized hardware. By integrating robot mobility with deep learning, Xue's approach enables more adaptive and scalable localization solutions that can operate in dynamic indoor environments. His research has significant implications for autonomous robotics, smart building management, and location-based services. With 13 citations to date, this work is gaining traction among researchers seeking practical, infrastructure-light alternatives to traditional GPS-denied navigation systems. Xue's contributions are particularly valuable for advancing the reliability and accuracy of Wi-Fi-based positioning in real-world applications.
Research Focus
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Top Papers
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