An Approach for GCI Fusion With Labeled Multitarget Densities

TL;DR

This paper introduces a novel GCI fusion approach for labeled multi-target densities that handles label inconsistencies across agents, improving multi-target tracking accuracy in complex scenarios.

Contribution

It proposes a joint label space for GCI fusion, avoiding label consistency constraints, and introduces a simplified method for well-separated targets.

Findings

Effective handling of label inconsistency demonstrated

High performance in challenging tracking scenarios

Simplified fusion method for well-separated targets

Abstract

This paper addresses the Generalized Covariance Intersection (GCI) fusion method for labeled random finite sets. We propose a joint label space for the support of fused labeled random finite sets to represent the label association between different agents, avoiding the label consistency condition for the label-wise GCI fusion algorithm. Specifically, we devise the joint label space by the direct product of all label spaces for each agent. Then we apply the GCI fusion method to obtain the joint labeled multi-target density. The joint labeled RFS is then marginalized into a general labeled RFS, providing that each target is represented by a single Bernoulli component with a unique label. The joint labeled GCI (JL-GCI) for fusing LMB RFSs from different agents is demonstrated. We also propose the simplified JL-GCI method given the assumption that targets are well-separated in the scenario. The simulation result presents the effectiveness of label inconsistency and excellent performance in challenging tracking scenarios.