Cooperative Parameter Tracking on the Unit Sphere Using Distributed Adapt-Then-Combine Particle Filters and Parallel Transport

This paper introduces a new distributed Adapt-then-Combine (ATC) diffusion algorithm for cooperative tracking of an un-known state vector that evolves on the unit hypersphere. The adapt step is implemented for a general nonlinear observation model and a dynamic state model defined on the hypersphere...

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Bibliographic Details
Published inICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 5564 - 5568
Main Authors de Figueredo, Caio G., Bordin, Claudio J., Bruno, Marcelo G. S.
Format Conference Proceeding
LanguageEnglish
Published IEEE 06.06.2021
Subjects
Adaptation models
Approximation algorithms
Diffusion methods
Distributed particle filters
Manifolds
Parallel transport
Receivers
Signal processing
Signal processing algorithms
Simulation
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Summary:This paper introduces a new distributed Adapt-then-Combine (ATC) diffusion algorithm for cooperative tracking of an un-known state vector that evolves on the unit hypersphere. The adapt step is implemented for a general nonlinear observation model and a dynamic state model defined on the hypersphere using a marginal particle filter (PF). The combine step in turn uses parallel transport to build Gaussian parametric approximations on a common tangent space to the spherical manifold. Performance results are compared to those of competing linear diffusion Extended Kalman Filters and non-cooperative PFs.
ISSN:2379-190X
DOI:10.1109/ICASSP39728.2021.9414948