Tracking Multiple Targets Using Binary Decisions From Wireless Sensor Networks

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.