Generalized Labeled Multi-Bernoulli Filters and Multitarget-Correlation Models

TL;DR

This paper extends the GLMB filter framework to incorporate statistical correlations among targets, enabling more accurate multitarget tracking in clustered scenarios with small groups of closely-spaced targets.

Contribution

It introduces a generalized interpretation of GLMB p.d.f.'s for correlated targets and reformulates the filter to handle simple labeled correlation models.

Findings

GLMB p.d.f.'s can model correlated target populations.

The SLC-GLMB filter effectively handles target clusters.

Suitable for small, closely-spaced target groups.

Abstract

The generalized labeled multi-Bernoulli (GLMB) filter is a theoretically rigorous Bayes-optimal multitarget tracking algorithm with computationally tractable implementations, based on labeled random finite set (LRFS) theory. It presumes that multitarget populations can be approximated using GLMB multitarget probability density functions (p.d.f.'s), which consist of weighted hypotheses regarding the current target-states. A special case of the GLMB p.d.f.-the LMB p.d.f.-presumes that the targets are statistically independent. This paper demonstrates that a) GLMB p.d.f.'s can be interpreted as straightforward generalizations of LMB p.d.f.'s to statistically correlated target populations, given an implicit presumption of "simple labeled correlation" (SLC) models of multitarget correlation; b) the GLMB filter can be reformulated as a SLC-GLMB filter; and c) SLC models seem primarily appropriate for target clusters consisting of small numbers of closely-spaced targets.