Efficient centralised and decentralised Gaussian process approaches for online tracking within Stone Soup

Lyu, C., Liu, X., Wright, J. et al. (3 more authors) (2024) Efficient centralised and decentralised Gaussian process approaches for online tracking within Stone Soup. In: Proceedings of the 2024 27th International Conference on Information Fusion (FUSION). 2024 27th International Conference on Information Fusion (FUSION), 08-11 Jul 2024, Venice, Italy. Institute of Electrical and Electronics Engineers (IEEE) ISBN 9798350371420

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Item Type: Proceedings Paper
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© 2024 The Authors. Except as otherwise noted, this author-accepted version of a paper published in Proceedings of the 2024 27th International Conference on Information Fusion (FUSION) is made available via the University of Sheffield Research Publications and Copyright Policy under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/

Keywords: Learning Gaussian process methods; Distributed Gaussian process; sensor networks; covariance matrix; tracking; Stone Soup; online tracking
Dates:
  • Published: 11 October 2024
  • Published (online): 11 October 2024
  • Accepted: 1 May 2024
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:
Funder
Grant number
ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL
EP/T013265/1
Engineering and Physical Sciences Research Council
EP/T013265/1
UNITED STATES DEPARTMENT OF DEFENSE
UNSPECIFIED
Depositing User: Symplectic Sheffield
Date Deposited: 05 Jun 2024 08:36
Last Modified: 21 Oct 2024 14:44
Status: Published
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Refereed: Yes
Identification Number: 10.23919/FUSION59988.2024.10706478
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