Liu, X. and Mihaylova, L. orcid.org/0000-0001-5856-2223 (2024) Active sensing for target tracking: a Bayesian optimisation approach. 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 9781737749769
Abstract
Active sensing plays an essential role in searching and tracking a target without initial target state information. This paper studies the active sensing approach for sensor management problems using multiple unmanned aerial vehicles based on the received signal strength measurements of the target. A Bayesian optimisation-based approach is proposed which adopts the Gaussian process method to model the received signal strength in an area over time and then the expected improvement acquisition function is leveraged to decide where to take new measurements considering the uncertainty of the Gaussian process. A unique contribution of this paper consists of the designed spatial-temporal composite kernel function that accounts for the time-varying nature of the signal strength. Numerical results obtained from different measurement noise levels and varying initial Bayesian optimisation settings demonstrate that the proposed approach can efficiently schedule multiple unmanned aerial vehicles to locate the target within a minimum number of initial data. Particularly, it achieves at most 57% lower tracking error and 46% lower lost-track probability as compared to the benchmark approach.
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: | Bayesian optimisation; target tracking; sensor management; Gaussian process; unmanned aerial vehicles; active sensing |
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 ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL EP/S016813/1 ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL EP/V026747/1 UNITED STATES DEPARTMENT OF DEFENSE UNSPECIFIED |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 24 May 2024 15:29 |
Last Modified: | 21 Oct 2024 14:35 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Refereed: | Yes |
Identification Number: | 10.23919/FUSION59988.2024.10706282 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:212789 |
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Licence: CC-BY 4.0