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.

中文翻译：

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用于行人跟踪的具有自适应测距误差缓解的 INS 和 UWB 融合方法

融合技术用于行人跟踪以实现更准确和鲁棒的跟踪系统。一种常见的方法是将行人佩戴的惯性导航系统 (INS) 与基于无线电的系统相融合，以相互补充并减轻其缺点。尽管最先进的方法提高了准确性，但这些跟踪系统的部署复杂性和成本仍然是一个主要瓶颈。在本文中，提出了一种新颖的 INS 和超宽带 (UWB) 融合方法，该方法仅通过从放置在已知位置的 UWB 锚点获得的测距测量来补充 INS。提出了一种自适应UWB测距不确定性模型，并将其结合到粒子滤波器融合算法中，减少了UWB测量的误差，提高了定位精度。与具有可比跟踪性能的其他方法相比，所提出的方法显着降低了部署复杂性和成本。行人跟踪系统是使用智能手机的内置惯性测量单元和 DecaWave TREK1000 UWB 开发套件实现的。进行了两个实际的长距离行人跟踪实验以证明所提出方法的准确性和鲁棒性，与仅 INS 跟踪结果相比，平均位置误差降低了 73.23%。