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

Author(s)

de Figueredo, Caio G. ^{[1, 2]} ; Bordin Jr, Claudio J. ; Bruno, Marcelo G. S. ^[1]

Affiliation

^[1] Inst Tecnol Aeronaut, BR-12228900 Sao Jose Dos Campos, SP - Brazil

^[2] Escola Naval, BR-20021010 Rio De Janeiro, RJ - Brazil

• Document type: Journal article
• Source: IEEE SIGNAL PROCESSING LETTERS; v. 27, p. 715-719, 2020.
• Web of Science Citations: 0

Abstract

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 Opportunities: Regular Research Grants