Arithmetic Average Density Fusion -- Part IV: Distributed Heterogeneous Fusion of RFS and LRFS Filters via Variational Approximation

Abstract

This paper, the fourth part of a series of papers on the arithmetic average (AA) density fusion approach and its application for target tracking, addresses the intricate challenge of distributed heterogeneous multisensor multitarget tracking, where each inter-connected sensor operates a probability hypothesis density (PHD) filter, a multiple Bernoulli (MB) filter or a labeled MB (LMB) filter and they cooperate with each other via information fusion. Earlier papers in this series have proven that the proper AA fusion of these filters is all exactly built on averaging their respective unlabeled/labeled PHDs. Based on this finding, two PHD-AA fusion approaches are proposed via variational minimization of the upper bound of the Kullback-Leibler divergence between the local and multi-filter averaged PHDs subject to cardinality consensus based on the Gaussian mixture implementation, enabling heterogeneous filter cooperation. One focuses solely on fitting the weights of the local Gaussian components (L-GCs), while the other simultaneously fits all the parameters of the L-GCs at each sensor, both seeking average consensus on the unlabeled PHD, irrespective of the specific posterior form of the local filters. For the distributed peer-to-peer communication, both the classic consensus and flooding paradigms have been investigated. Simulations have demonstrated the effectiveness and flexibility of the proposed approaches in both homogeneous and heterogeneous scenarios.

arXiv comment13 pages,14 figures

References in corpus (15)

• Labeled Random Finite Sets and the Bayes Multi-Target Tracking Filter
• Marginal multi-Bernoulli filters: RFS derivation of MHT, JIPDA and association-based MeMBer
• Poisson multi-Bernoulli conjugate prior for multiple extended object filtering
• Second Order Statistics Analysis and Comparison between Arithmetic and Geometric Average Fusion
• Robust Distributed Fusion with Labeled Random Finite Sets
• Partial Consensus and Conservative Fusion of Gaussian Mixtures for Distributed PHD Fusion
• An efficient, variational approximation of the best fitting multi-Bernoulli filter
• Fusion of Probability Density Functions
• A Distributed Particle-PHD Filter with Arithmetic-Average PHD Fusion
• Distributed Multi-object Tracking under Limited Field of View Sensors
• Arithmetic Average Density Fusion -- Part II: Unified Derivation for Unlabeled and Labeled RFS Fusion
• Heterogeneous Multi-sensor Fusion with Random Finite Set Multi-object Densities
• Arithmetic Average Density Fusion -- Part I: Some Statistic and Information-theoretic Results
• An Overview of Multi-Object Estimation via Labeled Random Finite Set
• Arithmetic Average Density Fusion -- Part III: Heterogeneous Unlabeled and Labeled RFS Filter Fusion