论文标题
分散的可能性推论，对目标跟踪的应用
Decentralised possibilistic inference with applications to target tracking
论文作者
论文摘要
通过网络通过多个传感器进行融合和共享信息是一项具有挑战性的任务。这一挑战的一部分是由于缺乏融合概率分布的基础规则，其各种方法来自不同的原则。然而，当在可能性理论框架内表达跟踪算法时，可以证明一个特定的融合规则是准确的，因为它等同于非分布的可能性方法。在本文中，该融合规则基于Bernoulli滤波器的可能类似物，将该融合规则应用于分散融合。然后，我们证明所提出的方法在模拟数据上的表现优于其概率对应物。
Fusing and sharing information from multiple sensors over a network is a challenging task. 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 tracking algorithms within the framework of possibility theory, one specific fusion rule can be proved to be exact in the sense that it is equivalent to the non-distributed possibilistic approach. In this article, this fusion rule is applied to decentralised fusion, based on the possibilistic analogue of the Bernoulli filter. We then show that the proposed approach outperforms its probabilistic counterpart on simulated data.
