Jianwen Gan

Macau University of Science and Technology

Papers

Total Citations

H-Index

About

Jianwen Gan is a leading researcher in the field of indoor localization and navigation, with a particular focus on leveraging Wi-Fi infrastructure for precise positioning. His work addresses the critical challenge of accurate indoor tracking in environments where GPS is unavailable, combining robotics with deep learning to push the boundaries of what is possible. Gan's most cited paper, "Wi-Fi-Based Indoor Localization and Navigation: A Robot-Aided Hybrid Deep Learning Approach" (2023), has already garnered 13 citations, reflecting its timely impact on both industry and academia. In this work, he introduces a novel hybrid deep learning framework that integrates robotic assistance to enhance the robustness and accuracy of Wi-Fi-based localization systems. By tackling the inherent instability of Wi-Fi signals, Gan's contributions enable more reliable navigation for autonomous robots and mobile smart devices, with applications ranging from smart buildings to emergency response. His research stands at the intersection of ubiquitous computing and artificial intelligence, offering practical solutions that bridge the gap between theoretical models and real-world deployment.