Zhu, F., Huang, Y., Xue, C. et al. (2 more authors) (2022) A sliding window variational outlier-robust Kalman filter based on student’s t noise modelling. IEEE Transactions on Aerospace and Electronic Systems, 58 (5). pp. 4835-4849. ISSN 0018-9251
Abstract
Existing robust state estimation methods are generally unable to distinguish model uncertainties (state outliers) from measurement outliers as they only exploit the current measurement. In this paper, the measurements in a sliding window are therefore utilized to better distinguish them, and an adaptive method is embedded, leading to a sliding window variational outlier-robust Kalman filter based on Student's t noise modelling. Target tracking simulations and experiments show that the tracking accuracy and consistency of the proposed filter are superior to those of the existing state-of-the-art outlier-robust methods thanks to the improved ability to identify the outliers but at a cost of greater computational burden.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 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: | outlier-robust Kalman filter; outlier identification; sliding window; Student’s t distribution; variational Bayesian; target 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) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 04 Apr 2022 07:27 |
Last Modified: | 01 Apr 2023 00:13 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Refereed: | Yes |
Identification Number: | 10.1109/TAES.2022.3164012 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:185215 |