Indoor Positioning Techniques and Approaches for WI-FI Based Systems

Abstract

The rapid expansion of smartphones' market coupled with the advances in mobile computing technology has opened up doors for new mobile services and applications. Quite a few of these services require the knowledge of the exact location of their handsets. Although, existing global positioning systems (GPS) perform best in outdoor environments, they have poor performance indoors. This has initiated the need for a new generation of positioning systems. In this thesis, we focus on wireless local area networks (WLAN)-based indoor positioning systems to act as GPS counterpart indoors. More specifically, we study two received signal strength (RSS)-based positioning techniques, fingerprinting and propagation models. We shed light on the advantages of each technique and propose different methods to counteract their shortcomings. Namely, we propose a hybrid solution of clustering and fast search techniques to reduce the computational requirements of fingerprinting. In addition, we propose a dimensionality reduction technique to restrict the location fingerprints to signal strength values received from only informative Access Points (APs), hence to further reduce fingerprinting complexity. For this purpose, we implement a modified fast orthogonal search method to choose the most informative APs from the set of all hearable APs in the region. Finally, we propose an indoor localization system that integrates the RSS correction methods to enhance the positioning accuracy of propagation models. This proposed system aims to achieve accurate modeling of signals' propagation inside buildings without the need for expensive site surveys required for fingerprinting. Our experiments were conducted inside the engineering building at our university, using real RSS data. The obtained results show that the aforementioned first two proposed methods enhance fingerprinting techniques by reducing their computational complexity, while the third enhances the accuracy of propagation models.