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A Novel Measurement Processing Approach to the Parallel Expectation Propagation Unscented Kalman Filter
University of Sheffield, England.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
University of Sheffield, England.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering. Ericsson Research, Linkoping, Sweden.
2017 (English)In: 2017 20TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION), IEEE , 2017, 665-670 p.Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
IEEE , 2017. 665-670 p.
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-142453DOI: 10.23919/ICIF.2017.8009713ISI: 000410938300095ISBN: 978-0-9964-5270-0 OAI: oai:DiVA.org:liu-142453DiVA: diva2:1153620
Conference
20th International Conference on Information Fusion (Fusion)
Note

Funding Agencies|EC Seventh Framework Programme TRAcking in compleX sensor systems (TRAX) [607400]; UK Royal Society fund [E150823]

Available from: 2017-10-31 Created: 2017-10-31 Last updated: 2017-10-31

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf