In this work we present the design principles of a wearable positioning system for users in unprepared indoor environments. We describe the most suitable technology for our application and we model the dynamics of a walking user. The system uses low-cost inertial sensors and a location system based on ultrawideband (UWB). Data fusion is carried out with a Kalman filter.The user position is estimated from data provided by the UWB location system. To update the position and direction of the user we use a dead reckoning algorithm. The use of redundant sensors and the data fusion technique minimises the presence of shadow zones in the environment. We show the advantages of combining different sensors systems.
E. Pulido Herrera, R. Quiros, H. Kaufmann: "Analysis of a Kalman Approach for a Pedestrian Positioning System in Indoor Environments"; Talk: Proceedings of the European Conference on Parallel and Distributed Computing, Rennes, France; 08-28-2007 - 08-31-2007; in: "Proceedings of the European Conference on Parallel and Distributed Computing", (2007), ISSN: 0302-9743; 931 - 941.
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