Adaptive Anchor Pairs Selection in a TDOA-based System Through Robot Localization Error Minimization

TL;DR

This work tackles accuracy limitations in indoor UWB TDOA localization by introducing a zone-based, adaptive anchor-pairs selection method calibrated with a LiDAR-equipped robot. Regions are partitioned and, for each zone, candidate TDOA pair sets are evaluated against LiDAR-derived references to minimize RMSE, yielding zone-specific anchor configurations for runtime. The localization workflow uses a rough LS estimate with the prior zone’s set, then switches to the zone-optimized set and applies an Unscented Kalman Filter for final positioning, enabling iterative improvements. Across simulations and real-world apartment experiments, the approach yields substantial accuracy gains, achieving a median trajectory error around 25 cm for moving-person localization and outperforming fixed-anchor configurations, without strict LOS requirements.

Abstract

The following paper presents an adaptive anchor pairs selection method for ultra-wideband (UWB) Time Difference of Arrival (TDOA) based positioning systems. The method divides the area covered by the system into several zones and assigns them anchor pair sets. The pair sets are determined during calibration based on localization root mean square error (RMSE). The calibration assumes driving a mobile platform equipped with a LiDAR sensor and a UWB tag through the specified zones. The robot is localized separately based on a large set of different TDOA pairs and using a LiDAR, which acts as the reference. For each zone, the TDOA pairs set for which the registered RMSE is lowest is selected and used for localization in the routine system work. The proposed method has been tested with simulations and experiments. The results for both simulated static and experimental dynamic scenarios have proven that the adaptive selection of the anchor nodes leads to an increase in localization accuracy. In the experiment, the median trajectory error for a moving person localization was at a level of 25 cm.

Figures (9)

• Figure 1: The proposed system calibration method concept.
• Figure 2: The workflow of the proposed localization algorithm.
• Figure 3: The layout of the simulated environment. The numbers specify the zones into which the apartment was divided. The presented localization results were obtained based on different anchor pairs sets.
• Figure 4: Exemplary simulation results. Anchor pairs sets correspond to the sets for zones listed in Table \ref{['tab:best_pairs']}.
• Figure 5: Estimated cumulative distribution function of mean localization errors. Anchor pairs sets correspond to the sets for zones listed in Table \ref{['tab:best_pairs']}.