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The Marginalized Particle Filter for Automotive Tracking Applications
Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
Linköpings universitet, Institutionen för systemteknik, Reglerteknik. Linköpings universitet, Tekniska högskolan.
2005 (engelsk)Inngår i: Proceedings of the 2005 IEEE Intelligent Vehicles Symposium, 2005, s. 370-375Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
2005. s. 370-375
Emneord [en]
Automotive tracking, Non-linear state estimation, Extended Kalman filter, Marginalized particle filter, Marginalization
HSV kategori
Identifikatorer
URN: urn:nbn:se:liu:diva-14306DOI: 10.1109/IVS.2005.1505131ISBN: 0-7803-8961-1 (tryckt)OAI: oai:DiVA.org:liu-14306DiVA, id: diva2:23164
Konferanse
2005 IEEE Intelligent Vehicles Symposium, Las Vegas, NV, USA, June, 2005
Tilgjengelig fra: 2007-02-27 Laget: 2007-02-27 Sist oppdatert: 2013-04-16

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Eidehall, AndreasSchön, ThomasGustafsson, Fredrik

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