De Freitas, A., Fritsche, C., Mihaylova, L.S. orcid.org/0000-0001-5856-2223 et al. (1 more author) (2017) A Novel Measurement Processing Approach to the Parallel Expectation Propagation Unscented Kalman Filter. In: 2017 20th International Conference on Information Fusion (Fusion). 2017 20th International Conference on Information Fusion, 10-13 Jul 2017, Xi'an, China. IEEE ISBN 978-0-9964-5270-0
Abstract
Advances in sensor systems have resulted in the availability of high resolution sensors, capable of generating massive amounts of data. For complex systems to run online, the primary focus is on computationally efficient filters for the estimation of latent states related to the data. In this paper a novel method for efficient state estimation with the unscented Kalman Filter is proposed. The focus is on applications consisting of a massive amount of data. From a modelling perspective, this amounts to a measurement vector with dimensionality significantly greater than the dimensionality of the state vector. The efficiency of the filter is derived from a parallel filter structure which is enabled by the expectation propagation algorithm. A novel parallel measurement processing expectation propagation unscented Kalman filter is developed. The primary advantage of the novel algorithm is in the ability to achieve computational improvements with negligible loses in filter accuracy. An example of robot localization with a high resolution laser rangefinder sensor is presented. A 47.53% decrease in computational time was exhibited for a scenario with a processing platform consisting of 4 processors, with a negligible loss in accuracy.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017 IEEE. This is an author produced version of a paper subsequently published in 2017 20th International Conference on Information Fusion (Fusion). Uploaded 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) |
Funding Information: | Funder Grant number EUROPEAN COMMISSION - FP6/FP7 TRAX - 607400 ROYAL SOCIETY IE150823 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 09 Jun 2017 10:35 |
Last Modified: | 19 Dec 2022 13:36 |
Published Version: | https://doi.org/10.23919/ICIF.2017.8009713 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.23919/ICIF.2017.8009713 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:117250 |