WiFi Positioning with Mobility-Induced Graphs
Kyuwon Han, Seung Min Yu, Seong‐Lyun Kim, Seung‐Woo Ko · 2024
This paper introduces a novel approach, mobility-induced graph learning (MINGLE), to enhance the accuracy of Wi-Fi positioning. Traditional Wi-Fi positioning methods often struggle with accuracy due to obstructions and interference. MINGLE addresses these challenges by converting user movement patterns into graphs, which are then analyzed using graph neural network. This method involves creating two types of graphs, based on the time and direction of user mobility, and employs a novel cross-graph learning technique in conjunction with self-supervised learning. This approach has demonstrated significant improvements in positioning accuracy, achieving a remarkable accuracy of 1.301 (m) in an underground parking lot setting, without relying on labeled data samples.