超宽带锚节点定位的多跳自校准算法

Ben Van Herbruggen;Stijn Luchie;Jaron Fontaine;Eli De Poorter
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摘要

超宽带(UWB)是一种新兴的室内定位技术,具有高精度和良好的抗多径衰落和其他技术干扰的弹性。然而,UWB定位系统需要安装具有已知位置的基础设施设备(锚节点)作为参考点。这些坐标对于室内定位系统的性能至关重要,因为移动标签的位置将基于该信息来计算。目前,大多数大型系统都需要手动测量锚坐标,这是一个耗时且容易出错的过程。因此,我们提出了一种算法方法,根据对随机选择的小锚子集的位置的测量,通过使用双向测距UWB收集所有锚之间的距离来自动计算所有其他锚的位置。在本文中,我们提出了一个三阶段的算法,它包括:1)初始化阶段;2) 全局优化阶段;以及3)与移动节点的可选的额外校准阶段。与相关工作相比,我们的方法也适用于具有严重非视线效应的多跳环境。在以金属支架为障碍物和18个UWB节点的真实世界多跳工业4.0环境中,该算法能够定位锚,平均绝对误差仅为21.6cm。
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Multihop Self-Calibration Algorithm for Ultra-Wideband (UWB) Anchor Node Positioning
Ultra-wideband (UWB) is an emerging technology for indoor localization systems with high accuracy and excellent resilience against multipath fading and interference from other technologies. However, UWB localization systems require the installation of infrastructure devices (anchor nodes) with known positions to serve as reference points. These coordinates are of utmost importance for the performance of the indoor localization system as the position of the mobile tag(s) will be calculated based on this information. Currently most large-scale systems require manual measurement of the anchor coordinates, which is a time-consuming and error-prone process. Therefore, we propose an algorithmic approach whereby based on measurements of the position of a small random chosen subset of anchors, the position of all other anchors is calculated automatically by collecting distances between all anchors with two-way-ranging UWB. In this article we present a three stage algorithm which contains: 1) an initialization phase; 2) a global optimization phase; and 3) an optional extra calibration phase with a mobile node. In contrast to related work, our approach also works in multihop environments with severe non-line-of-sight effects. In a real world multihop Industry 4.0 environment with metal racks as obstacles and 18 UWB nodes, the algorithm is able to localize the anchors with an mean absolute error of only 21.6 cm.
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Table of Contents Front Cover Advancing Resilient and Trustworthy Seamless Positioning and Navigation: Highlights From the Second Volume of J-ISPIN IEEE Journal of Indoor and Seamless Positioning and Navigation Publication Information Enhancing Indoor Localization Accuracy in Dense IoT-Integrated 5GNR Networks: Introducing SGNCL for Sensor-Guided NLoS Correction Localization
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