Tracking, Identification and Classification of Random Finite Sets

Abstract

This paper considers the problem of joint multiple target tracking, identification, and classification. Standard approaches tend to treat the tasks of data association, estimation, track management and classification as separate problems. This paper outlines how it is possible to formulate a unified a Bayesian recursion for joint tracking, identification and classification. The formulation is based on the theory of random finite sets or finite set statistics, and specifically labeled random finite sets, which results in a propagation of a multi-target posterior which contains not only target information but all available track information. Implementations are briefly discussed. Where appropriate for particular applications this method can be considered Bayes optimal.