A Distributed Bayesian Data Fusion Algorithm With Uniform Consistency

• Published in: IEEE transactions on automatic control Vol. 69; no. 9; pp. 6176 - 6182
• Main Authors: Li, Yingke, Zhou, Enlu, Zhang, Fumin
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.09.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Ad hoc networks; Algorithms; Bayes methods; Bayesian analysis; Bayesian consistency; Bayesian learning; Consistency; Data integration; Data models; distributed data fusion; Multiple robots; Multisensor fusion; Network topologies; Network topology; Probability distribution; Robot sensing systems; Topology
• ISSN: 0018-9286; 1558-2523
• DOI: 10.1109/TAC.2024.3375254

Distributed data fusion methods, which possess guaranteed performance for ad hoc and arbitrarily connected networks, empower more scalable, flexible, and robust information fusion for multirobot sensor networks. This article proposes a novel distributed Bayesian data fusion algorithm, which ensures uniform consistency, i.e., all the locally estimated distributions converge to the true distribution, for arbitrary periodically connected communication graphs. Conservative fusion via the weighted exponential product (WEP) rule is utilized to combat inconsistencies that arise from double-counting common information between fusion agents, and the WEP fusion weight is chosen based on the dynamic communication network topology. The uniform consistency of the proposed algorithm is rigorously proved, and the cooperative consistency conditions that guarantee uniform consistency have been explicitly identified. The performance and convergence properties of the proposed algorithm are validated through simulations.