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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.