Aftab, W. and Mihaylova, L. orcid.org/0000-0001-5856-2223 (2020) On the impact of different kernels and training data on a Gaussian process approach for target tracking. In: Proceedings of 2020 IEEE 23rd International Conference on Information Fusion (FUSION). 2020 IEEE 23rd International Conference on Information Fusion (FUSION), 06-09 Jul 2020, Rustenburg, South Africa. IEEE , pp. 1-6. ISBN 9781728168302
Abstract
The application of multiple target tracking algorithms has exponentially increased during the last two decades. Recently, model-free approaches, such as Gaussian process regression and convolutional neural networks, have been developed for target tracking. This paper presents a simulation-based study on the practical aspects of a very promising and recently proposed Gaussian process method, namely the Gaussian process motion tracker [1]. The paper also provides design guidelines on the various aspects of the above-mentioned tracking method.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Target Tracking; Gaussian Process; Gaussian Process Motion Tracking; Nonlinear Estimation; Data Driven Methods |
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 Sciences Research Council EP/T013265/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 09 Jun 2020 07:23 |
Last Modified: | 10 Sep 2021 00:38 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.23919/FUSION45008.2020.9190413 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:161393 |