Huang, Y., Zhang, Y., Zhao, Y. et al. (2 more authors) (2020) Robust Rauch-Tung-Striebel smoothing framework for heavy-tailed and/or skew noises. IEEE Transactions on Aerospace and Electronic Systems, 56 (1). pp. 415-441. ISSN 0018-9251
Abstract
A novel robust Rauch-Tung-Striebel smoothing framework is proposed based on a generalized Gaussian scale mixture (GGScM) distribution for a linear state-space model with heavy-tailed and/or skew noises. The state trajectory, mixing parameters and unknown distribution parameters are jointly inferred using the variational Bayesian approach. As such, a major contribution of this work is unifying results within the GGScM distribution framework. Simulation and experimental results demonstrate that the proposed smoother has better accuracy than existing smoothers.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 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: | Rauch-Tung-Striebel smoother; heavy-tailed noise; heavy-tailed and skew noise; generalized Gaussian scale mixture distribution; variational Bayesian 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) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 01 May 2019 08:15 |
Last Modified: | 23 Nov 2021 15:16 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Refereed: | Yes |
Identification Number: | 10.1109/TAES.2019.2914520 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:145188 |