Overcoming radio map degradation in Wi-Fi-based positioning systems

Abstract

Wi-Fi-based positioning systems, particularly the ones based on Wi-Fi fingerprinting, rely on a Radio Map (RM) which represents the radio environment at the time when it was collected. Over time, phenomena such as the propagation effects or adding/removing Access Points (APs) from an indoor environment may lead to significant variations in the radio environment, thus leading to errors in estimated positions. Although it is common knowledge that RMs degrade over time, it is difficult to predict and detect when degradation causes large errors. In this paper, we propose a method that continuously monitors the radio environment and uses Radial Basis Functions (RBF) interpolation to automatically enrich an old RM with new information. Before enriching the RM, AP selection is performed to remove APs that disappeared and mobile APs from the old radio map. Then, the analysis of the radio environment is performed to select newly detected APs to enrich the radio map, based on predefined criteria. Our experiments with real-world data show a significant improvement over 100% in mean error when using the enriched RM. This approach presents a promising solution to overcome the RM degradation in Wi-Fi fingerprinting, with potential applications in indoor positioning and location-based services.