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

This paper addresses the problem of distributed multitarget detection and tracking based on the linear arithmetic average (AA) fusion. We first analyze the conservativeness and Fréchet mean properties of the AA fusion, presenting new analyses based on a literature review. Second, we propose a target-wise fusion rule 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 MB 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. Third, two communicatively and computationally efficient cardinality consensus approaches are presented which merely disseminate and fuse target existence probabilities among local MB filters. Finally, the performance of these four approaches in terms of accuracy, computing efficiency, and communication cost is tested in two simulation scenarios.