  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.
