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Estimating the location of people and tracking them in an indoor environment poses a fundamental challenge in ubiquitous computing. The accuracy of explicit positioning sensors such as GPS is often limited for indoor environments. This paper evaluates the feasibility of building an indoor location tracking system that is cost effective for large scale deployments, can operate over existing Wi-Fi networks, and can provide flexibility to accommodate new sensor observations as they become available. At the core of the system is a novel location and tracking algorithm using a Sigma-Point Kalman Smoother (SPKS) based Bayesian inference approach? The proposed SPKS fuses a predictive model of human walking with a number of low-cost sensors to track 2D position and velocity.
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