Labeled Random Finite Sets and Multi-Object Conjugate Priors

Abstract

The objective of multi-object estimation is to simultaneously estimate the number of objects and their states from a set of observations in the presence of data association uncertainty, detection uncertainty, false observations, and noise. This estimation problem can be formulated in a Bayesian framework by modeling the (hidden) set of states and set of observations as random finite sets (RFSs) that covers thinning, Markov shifts, and superposition. A prior for the hidden RFS together with the likelihood of the realization of the observed RFS gives the posterior distribution via the application of Bayes rule. We propose a new class of RFS distributions that is conjugate with respect to the multiobject observation likelihood and closed under the Chapman-Kolmogorov equation. This result is tested on a Bayesian multi-target tracking algorithm.