Shen, C. and Mihaylova, L. orcid.org/0000-0001-5856-2223 (2020) A flexible robust student’s t-based multimodel approach with maximum Versoria criterion. Signal Processing, 182. 107941. ISSN 0165-1684
Abstract
The performance of the state estimation for Gaussian state space models can be degraded if the models are affected by the non-Gaussian process and measurement noises with uncertain degree of non-Gaussianity. In this paper, we propose a flexible robust Student's t multi-model approach. More specifically, the degrees of freedom parameter from the Student's t distribution is assumed unknown and modelled by a Markov chain of state values. In order to capture more information of the Student's t distributions propagated through multiple models, we establish a model-based Versoria cost function in the form of a weighted mixture rather than the original form, and maximize the function to interact and fuse the multiple models. Simulated results prove the flexibility of the robustness of the proposed Student's t multi-model approach when the existence probability of the outliers is uncertain.
Metadata
Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). | ||||
Keywords: | Robustness; Flexibility; Versoria function; Degrees of freedom | ||||
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: |
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Depositing User: | Symplectic Sheffield | ||||
Date Deposited: | 17 Dec 2020 13:58 | ||||
Last Modified: | 04 Jan 2021 11:49 | ||||
Status: | Published | ||||
Publisher: | Elsevier | ||||
Refereed: | Yes | ||||
Identification Number: | https://doi.org/10.1016/j.sigpro.2020.107941 |