From Error Analysis to Mitigation: A Hybrid Framework for Enhancing Wi-Fi FTM Positioning in Multipath-Prone Indoor Scenarios

With the rapid growth in demand for indoor positioning, Wi-Fi Fine Time Measurement (FTM)-based positioning technology has gained significant attention due to its low cost and wide applicability. However, the positioning accuracy is significantly affected by the ranging errors in Wi-Fi. This paper proposes a comprehensive Wi-Fi FTM positioning framework to address this issue. Based on a systematic analysis of Wi-Fi error propagation mechanisms, correction and optimization solutions were developed through determining initial biases and modeling systematic ranging errors, predicting multipath interference via regression, and determining weights assisted by quality classification. Static and kinematic positioning experiments were conducted in an underground garage to validate the model’s generalization performance and applicability across various observation conditions. Results demonstrate that the distance model alone improved the ranging accuracy by 24.68%, and the regression model further enhanced it to 64.00%. By integrating correction and classification strategies, the proposed solution achieved a positioning accuracy of 0.80 m in general static environments and 1.20 m in complex environments. In kinematic experiments, the technique maintained optimal positioning accuracy under complex occlusion conditions, highlighting its robustness and practicality.