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
Abstract
This paper explores the application of centralised and distributed Gaussian process algorithms to real-time target tracking and compares their performance. By embedding the algorithms into the Stone Soup, the focus is on the innovative implementation of Gaussian process methods with learning hyperparameters and implementation with a factorised variance of the Gaussian kernel. The performance of the methods with different kernels was evaluated, not only with the Gaussian kernel. Extensive experiments with various kernel configurations demonstrate their importance in enhancing prediction accuracy and efficiency, especially in real-time tracking. The case studies with manoeuvring targets show significant advancements in tracking capabilities, particularly in wireless sensor networks, using optimised Gaussian process methods. This work advances Stone Soup’s capabilities and lays the groundwork for future investigations into adaptive Gaussian Process applications in tracking and sensor data analysis.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 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: |
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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 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:212990 |
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Licence: CC-BY 4.0