Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • 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
Magnetic Odometry - A Model-Based Approach Using A Sensor Array
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-1971-4295
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
2018 (English)In: 2018 21st International Conference on Information Fusion (FUSION), 2018, p. 794-798Conference paper, Published paper (Refereed)
Abstract [en]

A model-based method to perform odometry using an array of magnetometers that sense variations in a local magnetic field is presented. The method requires no prior knowledge of the magnetic field, nor does it compile any map of it. Assuming that the local variations in the  magnetic field can be described by a curl and divergence free polynomial model, a maximum likelihood estimator is derived. To gain insight into the array design criteria and the achievable estimation performance, the identifiability conditions of the estimation problem are analyzed and the Cramér-Rao bound for the one-dimensional case is derived. The analysis shows that with a second-order model it is sufficient to have six magnetometer triads in a plane to obtain local identifiability. Further, the Cramér-Rao bound shows that the estimation error is inversely proportional to the ratio between the rate of change of the magnetic field and the noise variance, as well as the length scale of the array. The performance of the proposed estimator is evaluated using real-world data. The results show that, when there are sufficient variations in the magnetic field, the estimation error is of the order of a few percent of the displacement. The method also outperforms current state-of-theart method for magnetic odometry

Place, publisher, year, edition, pages
2018. p. 794-798
Keywords [en]
Localization; Estimation
National Category
Control Engineering Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-149917DOI: 10.23919/ICIF.2018.8455430ISBN: 978-0-9964527-6-2 (electronic)ISBN: 978-1-5386-4330-3 (print)OAI: oai:DiVA.org:liu-149917DiVA, id: diva2:1236808
Conference
21st International Conference on Information Fusion, Cambridge, UK, July 10-13, 2018
Projects
CENIITAvailable from: 2018-08-06 Created: 2018-08-06 Last updated: 2018-09-24Bibliographically approved

Open Access in DiVA

fulltext(515 kB)157 downloads
File information
File name FULLTEXT02.pdfFile size 515 kBChecksum SHA-512
f88a8d8a39d87659478d9e068a8fa3600a28abaaf649c77181784c96e1e716d14821fac1cf2c671c61fb90ce9ab3ef5e6467e5f5c3a5db0c203cf020569fde0d
Type fulltextMimetype application/pdf

Other links

Publisher's full text

Search in DiVA

By author/editor
Skog, IsaacHendeby, GustafGustafsson, Fredrik
By organisation
Automatic ControlFaculty of Science & Engineering
Control EngineeringSignal Processing

Search outside of DiVA

GoogleGoogle Scholar
Total: 157 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 243 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • 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