Bai, M., Huang, Y., Zhang, Y. et al. (2 more authors) (2019) A novel progressive Gaussian approximate filter with variable step size based on a variational Bayesian approach. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP-2019). IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 12-17 May 2019, Brighton, UK. IEEE ISBN 9781479981311
Abstract
The selection of step sizes in the progressive Gaussian ap- proximate filter (PGAF) is important, and it is difficult to se- lect optimal values in practical applications. Furthermore, in the PGAF, significant integral approximation errors are gener- ated by the repeated approximate calculations of the Gaussian weighted integrals, which results in an inaccurate measure- ment noise covariance matrix (MNCM). To solve these prob- lems, in this paper, the step sizes and the MNCM are jointly estimated based on the variational Bayesian (VB) approach. By incorporating the adaptive estimates of step sizes and the MNCM into the PGAF framework, a novel PGAF with vari- able step size is proposed. Simulation results illustrate that the proposed filter has higher estimation accuracy than exist- ing state-of-the-art nonlinear Gaussian approximate filters.
Metadata
Item Type: | Proceedings Paper |
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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: | Gaussian approximate filter; progressive measurement update; variable step size; variational Bayesian |
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: | 21 Feb 2019 13:16 |
Last Modified: | 17 Apr 2020 00:38 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/ICASSP.2019.8682907 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:142836 |