A Bayesian Method for Integrated Multitarget Tracking and Sensor Management

This paper presents a n integrated method for target tracking and sensor management, applied to the problem of tracking multiple ground targets. W e use a multiple target tracking methodology based on recursive estimation of a Joint Multitarget Probability Density (JMPD) which i s implemented using particle filtering methods. This Bayesian method for tracking multiple targets allows nonlinear, nonGaussian target motion and measurement-to-state coupling. The sensor management scheme i s predicated on maximizing the expected R i n y i Information Divergence between the current JMPD and the JMPD after a measurement has been made. The R iny i Information Divergence, a generalization of the Kullback-Leibler Distance, provides a way to measure the dissimilarity between two densities. Sensor management then proceeds by evaluating the expected information gain for each of the possible measurement decisions, and selecting to make the measurement that maximizes the expected information gain.