Accurately Tracking Relative Positions of Moving Trackers based on UWB Ranging and Inertial Sensing without Anchors
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 19 Sep 2024.
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Citing papers per year: 2025: 2; 2026: 1.
3 papers.
- 2025Human Motion Capture from Loose and Sparse Inertial Sensors with Garment-aware Diffusion Modelscited by 5
- 2025Group Inertial Poser: Multi-Person Pose and Global Translation from Sparse Inertial Sensors and Ultra-Wideband Rangingcited by 3
- 2026EgoExoMoCap: Distributed Ego-Exo Human Motion Capturecited by 1
- 2026EgoExoMoCap: Distributed Ego-Exo Human Motion Capture17 Jul 2026
- 2025Group Inertial Poser: Multi-Person Pose and Global Translation from Sparse Inertial Sensors and Ultra-Wideband Ranging24 Oct 2025
- 2025Human Motion Capture from Loose and Sparse Inertial Sensors with Garment-aware Diffusion Models18 Jun 2025