An INS and UWB Fusion Approach With Adaptive Ranging Error Mitigation for Pedestrian Tracking

Abstract

Fusion techniques are employed in pedestrian tracking to achieve more accurate and robust tracking systems. A common approach is to fuse Inertial Navigation System (INS), worn by a pedestrian, with a radio-based system to complement each other and mitigate their shortcomings. Despite the increased accuracy achieved in the state-of-the-art approaches, the deployment complexity and cost of these tracking systems remain a major bottleneck. In this paper, a novel INS and Ultra-wideband (UWB) fusion approach, which complements INS only with ranging measurements obtained from UWB anchors placed at known location, is proposed. An adaptive UWB ranging uncertainty model is proposed and incorporated in a Particle Filter fusion algorithm, which reduces errors of the UWB measurements and enhances positioning accuracy. The proposed approach achieves significant reduction of the deployment complexity and cost compared to other approaches that have comparable tracking performance. The pedestrian tracking system is implemented using the built-in inertial measurement unit of a smartphone and DecaWave TREK1000 UWB development kit. Two practical long-distance pedestrian tracking experiments are conducted to demonstrate the accuracy and robustness of the proposed approach, which reduces mean position error up to 73.23 % when compared to INS only tracking results.