Distributed Fusion with Multi-Bernoulli Filter based on Generalized Covariance Intersection

Abstract

In this paper, we propose a distributed multi-object tracking algorithm through the use of multi-Bernoulli (MB) filter based on generalized Covariance Intersection (G-CI). Our analyses show that the G-CI fusion with two MB posterior distributions does not admit an accurate closed-form expression. To solve this challenging problem, we firstly approximate the fused posterior as the unlabeled version of $\delta$-generalized labeled multi-Bernoulli ($\delta$-GLMB) distribution, referred to as generalized multi-Bernoulli (GMB) distribution. Then, to allow the subsequent fusion with another multi-Bernoulli posterior distribution, e.g., fusion with a third sensor node in the sensor network, or fusion in the feedback working mode, we further approximate the fused GMB posterior distribution as an MB distribution which matches its first-order statistical moment. The proposed fusion algorithm is implemented using sequential Monte Carlo technique and its performance is highlighted by numerical results.

14 pages, 13 figures, under review for IEEE Trans. on Signal Process Volume: 65, Issue: 1, Jan.1, 1 2017

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• Arithmetic Average Density Fusion -- Part II: Unified Derivation for Unlabeled and Labeled RFS Fusion
• Multi-Sensor Control for Multi-Object Bayes Filters
• Heterogeneous Multi-sensor Fusion with Random Finite Set Multi-object Densities
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• Distributed Multi-Sensor Fusion Using Generalized Multi-Bernoulli Densities
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• Statistical Information Fusion for Multiple-View Sensor Data in Multi-Object Tracking
• Distributed Weighted Least Squares Estimator Based on ADMM
• Efficient approximations of the multi-sensor labelled multi-Bernoulli filter
• Distributed multi-view multi-target tracking based on CPHD filtering
• An Approach for GCI Fusion With Labeled Multitarget Densities
• The Mean of Multi-Object Trajectories