Tracking multiple targets using binary decisions from wireless sensor networks
Date of Original Version
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. © 2013 American Statistical Association.
Journal of the American Statistical Association
Katenka, Natallia, Elizaveta Levina, and George Michailidis. "Tracking multiple targets using binary decisions from wireless sensor networks." Journal of the American Statistical Association 108, 502 (2013): 398-410. doi:10.1080/01621459.2013.770284.