Joint multiple target tracking and classification in collaborative sensor networks

We address the problem of jointly tracking and classifying several targets within a sensor network where false detections are present. A collaborative signal processing algorithm where multiple targets are dynamically associated with leader nodes is presented. It is assumed that each target belongs to one of several classes and that the class information leads to the motion model of a target. We propose an algorithm based on sequential Monte Carlo (SMC) filtering of jump Markov systems to jointly track the system dynamic and classify the targets. Furthermore, an optimal sensor selection scheme based on the maximization of the expected mutual information is integrated naturally within the SMC tracking framework. Simulation results have illustrated the excellent performance of the proposed scheme.