Introduces a principled decentralised possibilistic fusion rule proven asymptotically exact for the Bernoulli filter that maintains local posterior independence and outperforms probabilistic baselines in cardinality and localisation error.
Monte Carlo optimization of decentralized estimation networks over directed acyclic graphs under communication constraints
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Decentralised possibilistic inference with applications to target tracking
Introduces a principled decentralised possibilistic fusion rule proven asymptotically exact for the Bernoulli filter that maintains local posterior independence and outperforms probabilistic baselines in cardinality and localisation error.