Papers
1
Total Citations
15
H-Index
1
About
Mengqi Li is a leading researcher in indoor positioning systems, with a primary focus on ultra-wideband (UWB) technology and optimization algorithms. Their 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. Li's major contribution lies in developing a hybrid optimization approach that combines genetic annealing with clustering analysis to enhance the accuracy of time-of-arrival (TOA) based UWB positioning. This innovative algorithm effectively mitigates the impact of multipath interference and signal blockage, achieving superior error reduction compared to conventional methods. By improving the reliability of indoor wireless positioning, Li's work directly supports the advancement of autonomous robotics, smart logistics, and IoT-enabled environments. Their research demonstrates a strong commitment to solving real-world localization challenges, making them a notable figure in the field of indoor navigation and sensor fusion.
Research Focus
Key Achievements
Top Papers
- 1