无需固定基站，用移动节点实现厘米级相对定位。
Accurately Tracking Relative Positions of Moving Trackers based on UWB Ranging and Inertial Sensing without Anchors
- 融合惯性传感与超宽带测距，构建多阶段滤波流程。
- 2D定位误差10.2cm，3D误差21.7cm，支持遮挡场景。
- 适合无基础设施环境，如移动团队或动态作业场景。
我们提出一种完全基于移动跟踪节点的相对定位系统，无需固定参考锚点。每个节点集成9自由度磁力与惯性测量单元及单天线超宽带无线电。设计了一套多阶段滤波流程，用于估计组内所有节点的相对布局。方法核心在于将自定义扩展卡尔曼滤波（EKF）与多维缩放（MDS）优化步骤结合，并将MDS输出反馈至EKF，形成动态反馈环以提升估计鲁棒性。同时，设计了可支持节点动态加入与离开的超宽带测距协议。在持续运动节点的实验中，系统在2D环境下相对定位误差为10.2cm，3D环境下为21.7cm，且在存在视线遮挡情况下仍保持稳定。本方法无需外部基础设施，特别适用于无法部署固定设备的场景。
原文摘要 · Abstract (English)
We present a tracking system for relative positioning that can operate on entirely moving tracking nodes without the need for stationary anchors. Each node embeds a 9-DOF magnetic and inertial measurement unit and a single-antenna ultra-wideband radio. We introduce a multi-stage filtering pipeline through which our system estimates the relative layout of all tracking nodes within the group. The key novelty of our method is the integration of a custom Extended Kalman filter (EKF) with a refinement step via multidimensional scaling (MDS). Our method integrates the MDS output back into the EKF, thereby creating a dynamic feedback loop for more robust estimates. We complement our method with UWB ranging protocol that we designed to allow tracking nodes to opportunistically join and leave the group. In our evaluation with constantly moving nodes, our system estimated relative positions with an error of 10.2cm (in 2D) and 21.7cm (in 3D), including obstacles that occluded the line of sight between tracking nodes. Our approach requires no external infrastructure, making it particularly suitable for operation in environments where stationary setups are impractical.
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