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On Arithmetic Average Fusion and Its Application for Distributed Multi-Bernoulli Multitarget Tracking

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posted on 2020-01-27, 19:24 authored by Tiancheng LiTiancheng Li, Xiaoxu Wang, Yan Liang, Quan Pan
Recently, the simple arithmetic averages (AA) fusion has demonstrated promising, even surprising, performance for multitarget information fusion. In this paper, we first analyze the conservativeness and Frechet mean properties of it, presenting new empirical analysis based on a comprehensive literature review. Then, we propose a target-wise fusion principle for tailoring the AA fusion to accommodate the multi-Bernoulli (MB) process, in which only significant Bernoulli components, each represented by an individual Gaussian mixture, are disseminated and fused in a Bernoulli-to-Bernoulli (B2B) manner. For internode communication, both the consensus and flooding schemes are investigated, respectively. At the core of the proposed fusion algorithms, Bernoulli components obtained at different sensors are associated via either clustering or pairwise assignment so that the MB fusion problem is decomposed to parallel B2B fusion subproblems, each resolved via exact Bernoulli-AA fusion. Two communicatively and computationally efficient cardinality consensus approaches are also presented which merely disseminate and fuse target existence probabilities among local MB filters. The accuracy and computing and communication cost of these four approaches are tested in two large scale scenarios with different sensor networks and target trajectories.


Key Laboratory Foundation of National Defence Technology under Grant 61424010306

National Natural Science Foundation of China under grants 61790552, 61873205, 61873208 and 61672431


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Submitting Author's Institution

Northwestern Polytechnical University

Submitting Author's Country

  • China