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Bayesian Cramer-Rao Bound for Nonlinear Filtering with Dependent Noise Processes
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology. (Sensor Fusion)
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology. (Sensor Fusion)
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology. (Sensor Fusion)
2013 (English)In: 16th International Conference on Information Fusion (FUSION 2013), IEEE , 2013, 797-804 p.Conference paper, Published paper (Refereed)
Abstract [en]

The Bayesian Cramer Rao Bound (BCRB) is de­rived for nonlinear state space models with dependent process and measurement noise processes. It generalizes the previously BCRB for the case of dependent noise. Two different dependence structures appearing in literature are considered, leading to two different recursions for BCRB. The special cases of Gaussian noise, and linear models are presented separately. Simulations demonstrate that correct treatment of dependencies is important for both filtering algorithms and the BCRB.

Place, publisher, year, edition, pages
IEEE , 2013. 797-804 p.
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-103982ISI: 000341370000106ISBN: 978-605-86311-1-3 (print)OAI: oai:DiVA.org:liu-103982DiVA: diva2:693539
Conference
16th International Conference on Information Fusion(FUSION 2013), Istanbul, Turkey, July 9-12, 2013
Funder
eLLIIT - The Linköping‐Lund Initiative on IT and Mobile CommunicationsLinnaeus research environment CADICS
Available from: 2014-02-04 Created: 2014-02-04 Last updated: 2014-10-14

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Fritsche, CarstenSaha, SaikatGustafsson, Fredrik

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  • Other style
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Language
  • de-DE
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  • en-US
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  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
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  • text
  • asciidoc
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