Robust Distributed Fusion with Labeled Random Finite Sets

TL;DR

This paper introduces a robust distributed fusion method for multi-object tracking using labeled random finite sets, addressing label inconsistency issues in sensor networks with theoretical analysis and practical algorithms.

Contribution

It proposes a novel fusion approach that marginalizes labeled densities to unlabeled ones before fusion, improving robustness against label inconsistencies.

Findings

The method is robust to label mismatches between sensors.

Algorithms are developed for GLMB and related filters.

Numerical experiments demonstrate improved tracking performance.

Abstract

This paper considers the problem of the distributed fusion of multi-object posteriors in the labeled random finite set filtering framework, using Generalized Covariance Intersection (GCI) method. Our analysis shows that GCI fusion with labeled multi-object densities strongly relies on label consistencies between local multi-object posteriors at different sensor nodes, and hence suffers from a severe performance degradation when perfect label consistencies are violated. Moreover, we mathematically analyze this phenomenon from the perspective of Principle of Minimum Discrimination Information and the so called yes-object probability. Inspired by the analysis, we propose a novel and general solution for the distributed fusion with labeled multi-object densities that is robust to label inconsistencies between sensors. Specifically, the labeled multi-object posteriors are firstly marginalized to their unlabeled posteriors which are then fused using GCI method. We also introduce a principled method to construct the labeled fused density and produce tracks formally. Based on the developed theoretical framework, we present tractable algorithms for the family of generalized labeled multi-Bernoulli (GLMB) filters including $\delta$-GLMB, marginalized $\delta$-GLMB and labeled multi-Bernoulli filters. The robustness and efficiency of the proposed distributed fusion algorithm are demonstrated in challenging tracking scenarios via numerical experiments.