Efficient factorisation-based Gaussian process approaches for online tracking

Lyu, C., Liu, X. and Mihaylova, L. orcid.org/0000-0001-5856-2223 (Accepted: 2022) Efficient factorisation-based Gaussian process approaches for online tracking. In: Proceedings of the 25th International Conference on Information Fusion (Fusion 2022). Fusion 2022 - The 25th International Conference on Information Fusion, 04-07 Jul 2022, Linköping, Sweden. Institute of Electrical and Electronics Engineers . (In Press)

Abstract

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Authors/Creators:
Copyright, Publisher and Additional Information: © 2022 The Authors. For the purpose of open access, the authors have applied a Creative Commons Attribution CC BY (http://creativecommons.org/licenses/by/4.0) licence to any Author Accepted Manuscript version arising.
Keywords: Gaussian process; sensor networks; uncertainty quantification; factorisation; covariance matrix; hierarchical off-diagonal matrix; low-rank approximation; Cholesky factorisation; online tracking
Dates:
  • Accepted: 4 April 2022
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield)
Funding Information:
FunderGrant number
US Army Research Laboratoryn/a
UK MOD University Defence Research Collaboration (UDRC)n/a
Depositing User: Symplectic Sheffield
Date Deposited: 06 Jun 2022 13:48
Last Modified: 06 Jun 2022 13:50
Status: In Press
Publisher: Institute of Electrical and Electronics Engineers
Refereed: Yes
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