A Fully Automated Distributed Multiple-Target Tracking and Identity Management Algorithm

In this paper, we consider the problem of tracking multiple targets and managing their identities in sensor networks. Each sensor is assumed to be able to track multiple targets, manage the identities of targets within its surveillance region, and communicate with its neighboring sensors. The problem is complicated by the fact that the number of targets within the surveillance region of a sensor changes over time. We propose a scalable distributed multiple-target tracking and identity management (DMTIM) algorithm that can track multiple targets and manage their identities efficiently in a distributed sensor network environment. DMTIM finds a globally consistent solution by maintaining local consistency among neighboring sensors. DMTIM consists of data association, multiple-target tracking, identity management, and information fusion. The data association and multiple-target tracking problems are efficiently solved by Markov chain Monte Carlo data association (MCMCDA) which can track an unknown number of targets. DMTIM manages identities of targets by incorporating local information and maintains local consistency among neighboring sensors via information fusion.