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Indoor localization with a significant degree of precision is extremely challenging. In this paper, we present a precise indoor localization approach based on novel particle filter and dynamic exclusion techniques.  The approach is compared with the Euclidean Distance probabilistic methods used for localization. The novelty of the proposed approach stems from its ability to fuse data collected from different sensor technologies to converge to more accurate distance estimation. Furthermore, the proposed approach is a pattern-based one that relies on empirical training data as opposed to closed-form mathematical models.

A Precise Indoor Localization Approach based on Particle Filter and Dynamic Exclusion Techniques