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Technical Report

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Technical Report Number



Tracking or localizing a moving target is a difficult task in a distributed sensor network, due to the lack of knowledge of the target's motion and signal noises. Most existing approaches to the problem use only sensory information and may require accurate target's motion models. In this paper, we present a Markovian approach that combines dynamically estimated target's motion models with received sensory information. Without a given motion model, this approach localizes a target in two steps, a location prediction step using dynamically generated motion models and a location correction step integrating sensory readings. Our experimental analysis shows that our approach leads to substantially more accurate and robust location estimations than the previous approaches using only sensory information. In addition, we characterize probabilistic conditions under which the estimation accuracy increases if more sensors are used, and the estimations converge to the target's real position asymptotically. We show an interesting relationship between the steps of location prediction and location correction, i.e., as more sensors are used, belief correction guarantees the estimations to converge to the target's real position at each step, and belief prediction accelerates the convergence.


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