RSSI localization with Gaussian Processes and Tracking

Location Fingerprinting (LF) is a promising localization technique that enables many commercial and emergency location-based services (LBS). While significant efforts have been invested in enhancing LF using advanced machine learning methods, the configuration effort required to deploy a LF system remains a significant issue. In this paper a practical LF system is proposed which employs Gaussian Processes (GP) to significantly reduce the required data base density. The GP solution is enhanced with a tracking algorithm which can easily incorporate floor plan constraints. The proposed system was prototyped with Android mobile phones in an enterprise environment. It is shown that with the proposed system an accuracy required for most commercial LBS applications can be achieved with a significantly reduced configuration effort.