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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Cooperative Parameter Estimation on the Unit Sphere Using a Network of Diffusion Particle Filters

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de Figueredo, Caio G. [1, 2] ; Bordin Jr, Claudio J. ; Bruno, Marcelo G. S. [1]
Total Authors: 3
[1] Inst Tecnol Aeronaut, BR-12228900 Sao Jose Dos Campos, SP - Brazil
[2] Escola Naval, BR-20021010 Rio De Janeiro, RJ - Brazil
Total Affiliations: 2
Document type: Journal article
Source: IEEE SIGNAL PROCESSING LETTERS; v. 27, p. 715-719, 2020.
Web of Science Citations: 0

We introduce in this paper novel Bayesian distributed estimation algorithms for tracking the hidden state of a system that evolves on a spherical manifold. In the proposed method, different nodes on a partially-connected network run particle filters (PFs) that assimilate local data and cooperate with their neighbors via Random Exchange (RndEx) and Adapt-then-Combine (ATC) diffusion techniques. To implement the diffusion filters, we introduce parametric approximations that abide by the geometric restrictions imposed on the state variables. Numerical simulations show that the proposed methodology outperforms equivalent non-cooperative PF algorithms and competing extended Kalman Filter (EKF) approaches. (AU)

FAPESP's process: 18/26191-0 - Bayesian Methods for Distributed Estimation in Cooperative Networks
Grantee:Marcelo Gomes da Silva Bruno
Support type: Regular Research Grants