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This paper introduces a framework for tracking multiple targets over time using binary decisions collected by a wireless sensor network, and applies the methodology to two case studies - an experiment involving tracking people and a project tracking zebras. The tracking approach is based on a penalized maximum likelihood framework, and allows for sensor failures, targets appearing and disappearing over time, and complex intersecting target trajectories. The authors show that binary decisions first corrected locally by a previously developed method known as local vote decision fusion provide the most robust performance in noisy environments, and give good tracking results in applications.
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