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Bayesian Filtering for Location Estimation
July-September 2003 (vol. 2 no. 3)
pp. 24-33
Dieter Fox, University of Washington
Jeffrey Hightower, University of Washington
Lin Liao, University of Washington
Dirk Schulz, University of Washington
Gaetano Borriello, University of Washington and Intel Research Seattle

Location awareness is important to many pervasive computing applications. Unfortunately, no location sensor takes perfect measurements or works well in all situations. So, it is crucial to represent uncertainty in sensed location information and combine information from different types of sensors. Bayesian-filter techniques provide a powerful statistical tool to help manage measurement uncertainty and perform multisensor fusion and identity estimation. In this article, the authors survey Bayes filter implementations and show their application to real-world location estimation tasks common in pervasive computing.

Index Terms:
Location awareness, sensors, Bayesian filter techniques, Bayes filters
Citation:
Dieter Fox, Jeffrey Hightower, Lin Liao, Dirk Schulz, Gaetano Borriello, "Bayesian Filtering for Location Estimation," IEEE Pervasive Computing, vol. 2, no. 3, pp. 24-33, July-Sept. 2003, doi:10.1109/MPRV.2003.1228524
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