Geo Referenced Dynamic Bayesian Networks for User Positioning on Mobile Systems

Boris Brandherm, Tim Schwartz

In: Thomas Strang , Claudia Linnhoff-Popien (Hrsg.). Proceedings of the First International Workshop on Location- and Context-Awareness (LoCA). International Workshop on Location- and Context-Awareness (LoCa-05) February 12-13 Oberpfaffenhofen Germany Seiten 223-234 Lecture Notes in Computer Sciences (LNCS) 3479 Springer Berlin, Heidelberg 2005.


The knowledge of the position of a user is valuable for a broad range of applications in the field of pervasive computing. Different techniques have been developed to cope with the problem of uncertainty, noisy sensors, and sensor fusion. In this paper we present a method, which is efficient in time- and space-complexity, and that provides a high scalability for in- and outdoor-positioning. The so-called geo referenced dynamic Bayesian networks enable the calculation of a user’s position on his own small hand-held device (e.g., Pocket PC) without a connection to an external server. Thus, privacy issues are considered and completely in the hand of the user.

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Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence