Continuous space estimation: Increasing WiFi-based indoor localization resolution without increasing the site-survey effort

Although lot of research has taken place in WiFi indoor localization systems, their accuracy can still be improved. When designing this kind of systems, fingerprint-based methods are a common choice. The problem with fingerprint-based methods comes with the need of site-surveying the environment which is effort-consuming. In this work, we propose an approach, based on Support Vector Regression, to estimate the Received Signal Strength at non site-surveyed positions of the environment. Experiments, performed in a real environment, show that the proposed method could be used to improve the resolution of fingerprint-based indoor WiFi localization systems without increasing the site-survey effort.

keywords: WiFi Indoor Localization, Fingerprinting, Continuous Space Estimation, Machine Learning, Location Based Services