Unsupervised and hybrid approaches for on-line RFID localization with mixed context knowledge

Abstract

Indoor localization of humans is still a complex problem, especially in resource-constrained environments, e. g., if there is only a small number of data available over time. We address this problem using active RFID technology and focus on room-level localization. We propose several unsupervised localization approaches and compare their accuracy to state-of-the art unsupervised and supervised localization methods. In addition, we combine unsupervised and supervised methods into a hybrid approach using different types of mixed context knowledge. We show, that the new unsupervised approaches significantly outperform state-of-the-art supervised methods, and that the hybrid approach performs best in our application setting. We analyze real world data collected at a two days evaluation of our working group management system MyGroup. © 2014 Springer International Publishing.