Li, X., Liu, Y., Mihaylova, L. orcid.org/0000-0001-5856-2223 et al. (3 more authors) (2020) Enhanced multiple model GPB2 filtering using variational inference. In: 2019 22th International Conference on Information Fusion (FUSION). 22nd International Conference on Information Fusion, 02-05 Jul 2019, Ottawa, Canada. IEEE ISBN 9781728118406
Abstract
Multiple model filtering has been widely used to handle uncertainties in system dynamics and noise characteristics in state estimation problems. The generalized pseudo-Bayesian filter of order 2 (GPB2) is a suboptimal multiple model state estimator. It achieves computational tractability via approximating each model-matched state posterior, which is a Gaussian mixture, with a single Gaussian density. This paper illustrates from the viewpoint of variational inference that this approximation affects the performance of GPB2 through the model probability update stage. An enhanced GPB2 algorithm is proposed. It takes into account the above approximation by applying a correction factor that is dependent on the Kullback-Leibler divergence (KLD) of the Gaussian mixture and single Gaussian density. A control variate-based Monte Carlo method for evaluating the KLD is developed. The upper and lower bounds for the desired KLD are derived to correct the Monte Carlo KLD result if it falls out of bounds. Simulations show that the enhanced GPB2 algorithm outperforms the original GPB2 and interacting multiple model (IMM) methods in maneuvering target tracking tasks.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 ISIF. 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. |
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: | 03 Jun 2019 13:52 |
Last Modified: | 27 Feb 2021 01:38 |
Published Version: | https://ieeexplore.ieee.org/abstract/document/9011... |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:146719 |