Tracking Multiple Targets Using Binary Decisions From Wireless Sensor Networks

• Autores: Natallia Katenka, Elizaveta Levina, George Michailidis
• Localización: Journal of the American Statistical Association, ISSN 0162-1459, Vol. 108, Nº 502, 2013, págs. 398-410
• Idioma: inglés
• DOI: 10.1080/01621459.2013.770284
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• Resumen

  • 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.