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Parametric Lower Bound for Nonlinear Filteringbased on Gaussian Process Regression Model
Linköping University. (Automatic Control)ORCID iD: 0000-0003-1214-2391
Linköping University. (Automatic control)
Ericsson AB.
2017 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Assessing the fundamental performance limitationsin Bayesian filtering can be carried out using the parametricCram´er-Rao bound (CRB). The parametric CRB puts a lowerbound on mean square error (MSE) matrix conditioned on aspecific state trajectory realization. In this work, we derive theparametric CRB for state-space models, where the measurementequation is modeled by a Gaussian process regression.These models appear, for instance in proximity report-basedpositioning, where proximity reports are obtained by hardthresholding of received signal strength (RSS) measurements, thatare modeled through Gaussian process regression. The proposedparametric CRB is evaluated on selected state trajectories andfurther compared with the positioning performance obtained bythe particle filter. The results corroborate that the positioningaccuracy achieved in this framework is close to the parametricCRB.

Place, publisher, year, edition, pages
2017.
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:liu:diva-140531OAI: oai:DiVA.org:liu-140531DiVA: diva2:1138472
Conference
International conference on information fusion
Available from: 2017-09-05 Created: 2017-09-05 Last updated: 2017-09-05

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Zhao, Yuxin
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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
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  • asciidoc
  • rtf