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Tracking Multiple Targets Using Binary Decisions From Wireless Sensor Networks
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DOI：10.1080/01621459.2013.770284.png)
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This article 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 dataset adapted from a project tracking zebras in Kenya. 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. We show that binary decisions about the presence/absence of a target in a sensor's neighborhood, corrected locally by a method known as local vote decision fusion, provide the most robust performance in noisy environments and give good tracking results in applications.
Keyword:
Binary data
Penalized maximum likelihood
Target tracking
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