Evaluation of Grid-based Uncertainty Propagation for Collaborative Self-Calibration in Indoor Positioning Systems

TL;DR

This paper empirically evaluates a grid-based Bayesian approach for collaborative self-calibration in indoor UWB positioning, reducing deployment costs while maintaining sub-meter accuracy.

Contribution

It extends a Bayesian grid-based method to improve UWB network calibration, demonstrating robustness and accuracy with real-world indoor data.

Findings

Achieved 0.28 m mean ranging error in line-of-sight conditions.

Maintained 1.11 m overall ranging error across mixed scenarios.

Demonstrated robustness to measurement noise and partial connectivity.

Abstract

Radio-based localization systems conventionally require stationary reference points (e.g. anchors) with precisely surveyed positions, making deployment time-consuming and costly. This paper presents an empirical evaluation of collaborative self-calibration for Ultra-Wideband (UWB) networks, extending a discrete Bayesian approach based on grid-based uncertainty propagation. The enhanced algorithm reduces measurement availability requirements while maintaining positioning accuracy through probabilistic state estimation. We validate the approach using real-world data from controlled indoor UWB network experiments with 12 nodes in a static environment. Experimental evaluation demonstrates 0.28~m mean ranging error under line-of-sight conditions and 1.11~m overall ranging error across mixed propagation scenarios, achieving sub-meter positioning accuracy. Results demonstrate the algorithm's robustness to measurement noise and partial connectivity scenarios typical in industrial deployments. The findings contribute to automated UWB network initialization for indoor positioning applications, reducing infrastructure dependency compared to manual anchor calibration procedures.