Tracking Multiple Targets Using Binary Decisions From Wireless Sensor Networks

• 成果类型: Article
• 署名作者: Katenka, Natallia; Levina, Elizaveta; Michailidis, George
• 署名单位: University of Rhode Island; University of Michigan System; University of Michigan
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2013.770284
• 发表日期: 2013
• 页码: 398-410
• 关键词: multitarget tracking; localization; selection; MODEL

摘要

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.