Situation-Aware Indoor Tracking with high-density, large-scale Wireless Sensor Networks

In this paper we propose an innovative approach to the problem of indoor position estimation that aims at extending tracking to a new level of “awareness” bringing to bear new ambient data and opening the possibility of “reasoning” not only on simple positioning but also on the situation at hand. In order to validate the approach, we implemented a positioning system called Situation-Aware Indoor Tracking (SAIT). The comparison of SAIT with several commercial systems highlights a promising behaviour, showing that exploiting the movement data (e.g. the users´ heading and speed) for updating the PF motion models used in the tracking engine together with situation assessment techniques can improve the accuracy of tracking up to 42% in comparison with a Wi-Fi based system.