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An adaptive PHD filter for tracking with unknown sensor characteristics
Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Automatic Control.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology. (Automatic Control)
2013 (English)Conference paper, Published paper (Refereed)
Abstract [en]

In multi-target tracking, the discrepancy between the nominal and the true values of the model parameters might result in poor performance. In this paper, an adaptive Probability Hypothesis Density (PHD) filter is proposed which accounts for sensor parameter uncertainty. Variational Bayes technique is used for approximate inference which provides analytic expressions for the PHD recursions analogous to the Gaussian mixture implementation of the PHD filter. The proposed method is evaluated in a multi-target tracking scenario. The improvement in the performance is shown in simulations.

Place, publisher, year, edition, pages
2013. 1736-1743 p.
Keyword [en]
variational Bayes; adaptive filtering; sensor calibration; probability hypothesis density filter; robust filtering; multiple target tracking
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-100232ISI: 000341370000231ISBN: 978-605-86311-1-3 (print)OAI: oai:DiVA.org:liu-100232DiVA: diva2:661053
Conference
Information Fusion (FUSION), 2013 16th International Conference on
Available from: 2013-10-31 Created: 2013-10-31 Last updated: 2014-10-14Bibliographically approved

Open Access in DiVA

VBPHD(473 kB)161 downloads
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Ardeshiri, TohidÖzkan, Emre
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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
  • text
  • asciidoc
  • rtf