Tracking Multiple Targets Using Binary Decisions From Wireless Sensor Networks

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.

KEY WORDS:

• Binary data
• Penalized maximum likelihood
• Target tracking

Acknowledgments

The authors thank the Editors, David Banks and Hal Stern, an Associate Editor, and two anonymous referees for many helpful comments. Special thanks are extended to Professor Songhwai Oh (UC Merced) for making the NEST data available and explaining the details of the experiment. E. Levina's research is partially supported by NSF grants DMS-1106772, DMS-1159005 and G. Michailidis’ by NSF grant DMS-1106695. This work was completed while the first author was a Ph.D. student at the University of Michigan.