Xiaotian Gao

Shandong University of Science and Technology

Papers

Total Citations

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About

Xiaotian Gao is a researcher specializing in indoor positioning systems and optimization algorithms, with a particular focus on Ultra-Wideband (UWB) technology. Gao’s most-cited work, “UWB indoor positioning optimization algorithm based on genetic annealing and clustering analysis” (2022, 15 citations), addresses a critical challenge in modern intelligent warehouse management and robot navigation: the significant positioning errors caused by indoor non-line-of-sight (NLOS) obstructions. By integrating genetic annealing with clustering analysis, Gao developed a novel algorithm that substantially improves the accuracy of time-of-arrival-based UWB positioning, directly enhancing the reliability of location-dependent automation systems. This contribution is particularly valuable for logistics and robotics applications where precise indoor location data is indispensable. Gao’s research bridges the gap between theoretical optimization methods and practical deployment challenges, offering a robust solution to the persistent problem of NLOS interference. With a growing citation record, Gao’s work is gaining recognition among engineers and researchers working on smart environments and autonomous navigation, positioning them as a promising contributor to the field of wireless localization and industrial IoT.