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The Marginalized Particle Filter for Automotive Tracking Applications
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
2005 (English)In: Proceedings of the 2005 IEEE Intelligent Vehicles Symposium, 2005, 370-375 p.Conference paper, Published paper (Refereed)
Abstract [en]

This paper deals with the problem of estimating the vehicle surroundings (lane geometry and the position of other vehicles), which is needed for intelligent automotive systems, such as adaptive cruise control, collision avoidance and lane guidance. This results in a nonlinear estimation problem. For automotive tracking systems, these problems are traditionally handled using the extended Kalman filter. In this paper we describe the application of the marginalized particle filter to this problem. Studies using both synthetic and authentic data show that the marginalized particle filter can in fact give better performance than the extended Kalman filter. However, the computational load is higher.

Place, publisher, year, edition, pages
2005. 370-375 p.
Keyword [en]
Automotive tracking, Non-linear state estimation, Extended Kalman filter, Marginalized particle filter, Marginalization
National Category
Engineering and Technology Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-14306DOI: 10.1109/IVS.2005.1505131ISBN: 0-7803-8961-1 (print)OAI: oai:DiVA.org:liu-14306DiVA: diva2:23164
Conference
2005 IEEE Intelligent Vehicles Symposium, Las Vegas, NV, USA, June, 2005
Available from: 2007-02-27 Created: 2007-02-27 Last updated: 2013-04-16

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Eidehall, AndreasSchön, ThomasGustafsson, Fredrik

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Citation style
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  • de-DE
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  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
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Output format
  • html
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  • asciidoc
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