|| COOPERATIVE PARAMETER ESTIMATION ON THE UNIT SPHERE USING A NETWORK OF DIFFUSION PARTICLE FILTERS
||Caio de Figueredo, Instituto Tecnológico de Aeronáutica, Brazil; Claudio Bordin, Universidade Federal do ABC, Brazil; Marcelo Bruno, Instituto Tecnológico de Aeronáutica, Brazil|
|Session||SPTM-3: Estimation, Detection and Learning over Networks 1|
|Session Time:||Tuesday, 08 June, 14:00 - 14:45|
|Presentation Time:||Tuesday, 08 June, 14:00 - 14:45|
|| Signal Processing Theory and Methods: [SSP] Statistical Signal Processing|
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|| 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.