Bayesian filtering for multi-object systems with independently generated observations

Abstract

A general approach for Bayesian filtering of multi-object systems is studied, with particular emphasis on the model where each object generates observations independently of other objects. The approach is based on variational calculus applied to generating functionals, using the general version of Faa di Bruno's formula for Gateaux differentials. This result enables us to determine some general formulae for the updated generating functional after the application of a multi-object analogue of Bayes' rule.