"UWB Indoor Localization Dataset for Federated Random-Forest Error Compensation AUTH- ELAB Dataset "

Abstract

"This dataset contains Ultra-Wideband (UWB) ranging and localization measurements collected for the evaluation of adaptive and federated learning methods for indoor positioning in challenging environments. The data were acquired in a controlled two-room laboratory deployment designed to capture realistic line-of-sight (LOS), non-line-of-sight (NLOS), multipath, and location-dependent ranging errors. Measurements include UWB anchor-to-tag distance estimates, corresponding reference positions, anchor information, and associated localization data suitable for training and evaluating range-error compensation and positioning algorithms.The dataset was developed in support of the study \u201cFederated Adaptive UWB Localization with Random-Forest Error Compensation\u201d and enables the investigation of centralized, distributed, and privacy-preserving machine-learning approaches for UWB localization. In particular, it can be used to study anchor-specific ranging-error correction, adaptive anchor weighting, geometric localization, random-forest regression, federated learning, and robustness to spatially nonuniform propagation conditions. The data are intended to facilitate reproducible research and comparative evaluation of indoor localization techniques for Internet-of-Things, industrial, edge-computing, and cyber-physical-system applications."