Universal Kriging of RSS Databases in a Bayesian Filter

Received signal strength (RSS) based navigation is chosen for many indoor propagation scenarios because of the high availability and low cost of the needed infrastructure. Usually, RSS-based navigation relies on fingerprinting techniques. TRe-cently, methods for the spatial interpolation of RSS databases, including ordinary and universal Kriging have become the focus of research, as they can enhance these databases. This paper explores theoretical possibility of using universal Kriging as a measurement model in a Bayesian filter to give the possibility of using RSS databases in a deeply coupled information fusion filter, with the potential of enhancing the performance of other positioning systems. The method is applied on a simplified propagation model and an exemplary implementation in an extended Kalman filter is presented in detail and evaluated.