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.
Possibility generalized labeled multi-bernoulli filter for multi-target tracking under epistemic uncertainty
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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.