Tracking multiple targets using binary decisions from wireless sensor networks
Document Type
Article
Date of Original Version
12-16-2013
Abstract
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.
Publication Title, e.g., Journal
Journal of the American Statistical Association
Volume
108
Issue
502
Citation/Publisher Attribution
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.
