Multiobject Tracking for Generic Observation Model Using Labeled Random Finite Sets

Abstract

This paper presents an exact Bayesian filtering solution for the multiobject tracking problem with the generic observation model. The proposed solution is designed in the labeled random finite set framework, using the product styled representation of labeled multiobject densities, with the standard multiobject transition kernel and no particular simplifying assumptions on the multiobject likelihood. Computationally tractable solutions are also devised by applying a principled approximation involving the replacement of the full multiobject density with a labeled multi-Bernoulli density that minimizes the Kullback-Leibler divergence and preserves the first-order moment. To achieve the fast performance, a dynamic-grouping-procedure-based implementation is presented with a step-by-step algorithm. The performance of the proposed filter and its tractable implementations are verified and compared with the state of the art in numerical experiments.