Robust Distributed Fusion with Labeled Random Finite Sets

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.

17pages, 23 figures

References in corpus (5)

• Generalized Labeled Multi-Bernoulli Approximation of Multi-Object Densities
• Distributed Fusion with Multi-Bernoulli Filter based on Generalized Covariance Intersection
• Robust Distributed Fusion with Labeled Random Finite Sets
• Consensus Labeled Random Finite Set Filtering for Distributed Multi-Object Tracking
• The Marginalized $\delta$-GLMB Filter

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• An Overview of Multi-Object Estimation via Labeled Random Finite Set
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• Arithmetic Average Density Fusion -- Part IV: Distributed Heterogeneous Fusion of RFS and LRFS Filters via Variational Approximation
• Distributed Joint Sensor Registration and Multitarget Tracking Via Sensor Network
• Distributed Poisson multi-Bernoulli filtering via generalised covariance intersection
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• An Approach for GCI Fusion With Labeled Multitarget Densities
• The Mean of Multi-Object Trajectories