Aftab, W. and Mihaylova, L. orcid.org/0000-0001-5856-2223 (2021) A learning gaussian process approach for maneuvering target tracking and smoothing. IEEE Transactions on Aerospace and Electronic Systems, 57 (1). pp. 278-292. ISSN 0018-9251
Abstract
Model-based approaches for target tracking and smoothing estimate the infinite number of possible target trajectories using a finite set of models. This paper proposes a data-driven approach that represents the possible target trajectories using a distribution over an infinite number of functions. Recursive Gaussian process and derivative based Gaussian process approaches for target tracking and smoothing are developed, with online training and parameter learning. The performance evaluation over two highly maneuvering scenarios, shows that the proposed approach provides 80% and 62% performance improvement in the position and 49% and 22% in the velocity estimation, respectively, as compared to the best model-based filter.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/. |
Keywords: | Target Tracking; Recursive Gaussian Process; Recursive Derivative based Gaussian Process; Parameter Learning |
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 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 27 Jul 2020 14:55 |
Last Modified: | 02 Jun 2021 13:35 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Refereed: | Yes |
Identification Number: | 10.1109/TAES.2020.3021220 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:163742 |