Decentralised possibilistic inference with applications to target tracking

TY - JOUR

T1 - Decentralised possibilistic inference with applications to target tracking

AU - Houssineau, Jeremie

AU - Cai, Han

AU - Uney, Murat

AU - Delande, Emmanuel

N1 - Publisher Copyright: © 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

PY - 2026/9

Y1 - 2026/9

N2 - Fusing and sharing information from multiple sensors over a network is a challenging task, partly due to the absence of a foundational rule for fusing probability distributions that preserves the independence of sources. To address this, we propose a decentralised inference framework based on possibility theory. Unlike probabilistic approaches that rely on ad-hoc averaging, we derive a principled fusion rule that is proven to be asymptotically exact, meaning it recovers the posterior of the optimal centralised possibilistic approach. We apply this rule to the possibilistic Bernoulli filter, leveraging its hierarchical nature to jointly infer data association and state estimation, distinct from standard decentralised Kalman filtering. We demonstrate that the proposed approach maintains the independence of local posteriors during fusion and, even under necessary approximations to handle Gaussian mixtures, significantly outperforms probabilistic geometric and arithmetic average fusion baselines in terms of cardinality and localisation error.

AB - Fusing and sharing information from multiple sensors over a network is a challenging task, partly due to the absence of a foundational rule for fusing probability distributions that preserves the independence of sources. To address this, we propose a decentralised inference framework based on possibility theory. Unlike probabilistic approaches that rely on ad-hoc averaging, we derive a principled fusion rule that is proven to be asymptotically exact, meaning it recovers the posterior of the optimal centralised possibilistic approach. We apply this rule to the possibilistic Bernoulli filter, leveraging its hierarchical nature to jointly infer data association and state estimation, distinct from standard decentralised Kalman filtering. We demonstrate that the proposed approach maintains the independence of local posteriors during fusion and, even under necessary approximations to handle Gaussian mixtures, significantly outperforms probabilistic geometric and arithmetic average fusion baselines in terms of cardinality and localisation error.

KW - Decentralised fusion

KW - Possibility theory

KW - Target tracking

UR - https://www.scopus.com/pages/publications/105035569329

U2 - 10.1016/j.sigpro.2026.110628

DO - 10.1016/j.sigpro.2026.110628

M3 - Article

AN - SCOPUS:105035569329

SN - 0165-1684

VL - 246

JO - Signal Processing

JF - Signal Processing

M1 - 110628

ER -