Liu, X., Mihaylova, L. orcid.org/0000-0001-5856-2223, George, J. et al. (1 more author) (2023) Gaussian process upper confidence bounds in distributed point target tracking over wireless sensor networks. IEEE Journal of Selected Topics in Signal Processing, 17 (1). pp. 295-310. ISSN 1932-4553
Abstract
Uncertainty quantification plays a key role in the development of autonomous systems, decision-making, and tracking over wireless sensor networks (WSNs). However, there is a need of providing uncertainty confidence bounds, especially for distributed machine learning-based tracking, dealing with different volumes of data collected by sensors. This paper aims to fill in this gap and proposes a distributed Gaussian process (DGP) approach for point target tracking and derives upper confidence bounds (UCBs) of the state estimates. A unique contribution of this paper includes the derived theoretical guarantees on the proposed approach and its maximum accuracy for tracking with and without clutter measurements. Particularly, the developed approaches with uncertainty bounds are generic and can provide trustworthy solutions with an increased level of reliability. A novel hybrid Bayesian filtering method is proposed to enhance the DGP approach by adopting a Poisson measurement likelihood model. The proposed approaches are validated over a WSN case study, where sensors have limited sensing ranges. Numerical results demonstrate the tracking accuracy and robustness of the proposed approaches. The derived UCBs constitute a tool for trustworthiness evaluation of DGP approaches. The simulation results reveal that the proposed UCBs successfully encompass the true target states with 88% and 42% higher probability in X and Y coordinates, respectively, when compared to the confidence interval-based method.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 The Author(s). This is an author-produced version of a paper subsequently published in IEEE Journal of Selected Topics in Signal Processing. This version is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0). |
Keywords: | Distributed learning; target tracking; wireless sensor networks; Gaussian process methods; uncertainty quantification; upper confidence bounds; trustworthy solutions |
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 UNITED STATES DEPARTMENT OF DEFENSE UNSPECIFIED |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 16 Nov 2022 17:22 |
Last Modified: | 23 Feb 2023 11:27 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Refereed: | Yes |
Identification Number: | 10.1109/JSTSP.2022.3223521 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:193228 |