A Catalog of Probability Generating Functionals for Multitarget Tracking and Data Assignment Problems

Abstract

This paper studies the class of Bayesian tracking filters for which the probability generating functional of the joint target-measurement process can be derived from the statistical assumptions that define the problem. The class includes filters for labeled and unlabeled targets, as well as hybrid filters in which both labeled and unlabeled targets are present. New results include the probability generating functional for interval filtering of target trajectories for both labeled an unlabeled targets, and a novel method for computing the probability generating function of measurement-to-target assignment probabilities. Probability generating functionals give exact expressions for calculating the importance weights in particle filter implementations. Low computational complexity approximations to the particle weights are derived from the probability generating functionals via the saddle point method.