Fusing and sharing information from multiple sensors over a network is a challenging task, especially in the context of multi-target tracking. Part of this challenge arises from the absence of a foundational rule for fusing probability distributions, with various approaches stemming from different principles. Yet, when expressing multi-target tracking algorithms within the framework of possibility theory, one specific fusion rule appears to be particularly natural and useful. In this article, this fusion rule is applied to both centralised and decentralised fusion, based on the possibilistic analogue of the probability hypothesis density filter. We then show that the proposed approach outperforms its probabilistic counterpart on simulated data.

中文翻译：

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多目标多传感器融合的可能框架

通过网络融合和共享来自多个传感器的信息是一项具有挑战性的任务，尤其是在多目标跟踪的情况下。这一挑战的一部分源于缺乏融合概率分布的基本规则，各种方法源于不同的原则。然而，当在可能性理论的框架内表达多目标跟踪算法时，一个特定的融合规则似乎特别自然和有用。在本文中，该融合规则适用于集中式和分散式融合，基于概率假设密度过滤器的可能性模拟。然后，我们表明，所提出的方法在模拟数据上优于其概率对应方法。