Tire Radii and Vehicle Trajectory Estimation Using a Marginalized Particle Filter
2011 (English)Report (Other academic)
Measurements of individual wheel speeds and absolute position from a global navigation satellite system (GNSS) are used for high-precision estimation of vehicle tire radii in this work. The radii deviation from its nominal value is modeled as a Gaussian process and included as noise components in a vehicle model. The novelty lies in a Bayesian approach to estimate online both the state vector of the vehicle model and noise parameters using a marginalized particle ﬁlter. No model approximations are needed such as in previously proposed algorithms based on the extended Kalman ﬁlter. The proposed approach outperforms common methods used for joint state and parameter estimation when compared with respect to accuracy and computational time. Field tests show that the absolute radius can be estimated with millimeter accuracy, while the relative wheel radius on one axleis estimated with submillimeter accuracy.
Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2011. , 10 p.
LiTH-ISY-R, ISSN 1400-3902 ; 3029
Marginalized particle filter, Tire radius, Conjugate prior, Noise parameter estimation
IdentifiersURN: urn:nbn:se:liu:diva-97967ISRN: LiTH-ISY-R-3029OAI: oai:DiVA.org:liu-97967DiVA: diva2:650838
FunderSwedish Research Council