liu.seSearch for publications in DiVA
Change search
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
Parametric Lower Bound for Nonlinear Filtering based on Gaussian Process Regression Model
Ericsson Research, Linkoping, Sweden.
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, 150-156 p.Conference paper, Published paper (Refereed)
Abstract [en]

Assessing the fundamental performance limitations in Bayesian filtering can be carried out using the parametric Cramer-Rao bound (CRB). The parametric CRB puts a lower bound on mean square error (MSE) matrix conditioned on a specific state trajectory realization. In this work, we derive the parametric CRB for state-space models, where the measurement equation is modeled by a Gaussian process regression. These models appear, for instance in proximity report-based positioning, where proximity reports are obtained by hard thresholding of received signal strength (RSS) measurements, that are modeled through Gaussian process regression. The proposed parametric CRB is evaluated on selected state trajectories and further compared with the positioning performance obtained by the particle filter. The results corroborate that the positioning accuracy achieved in this framework is close to the parametric CRB.

Place, publisher, year, edition, pages
IEEE , 2017. 150-156 p.
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:liu:diva-142450DOI: 10.23919/ICIF.2017.8009640ISI: 000410938300023ISBN: 978-0-9964-5270-0 OAI: oai:DiVA.org:liu-142450DiVA: diva2:1153623
Conference
20th International Conference on Information Fusion (Fusion)
Note

Funding Agencies|European Union Marie Curie training programme on Tracking in Complex Sensor Systems (TRAX) [607400]

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

Open Access in DiVA

No full text

Other links

Publisher's full text

Search in DiVA

By author/editor
Fritsche, Carsten
By organisation
Automatic ControlFaculty of Science & Engineering
Probability Theory and Statistics

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 3 hits
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