Tire Radii and Vehicle Trajectory Estimation Using a Marginalized Particle Filter
(English)Manuscript (preprint) (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 axle is estimated with submillimeter accuracy.
Engineering and Technology
IdentifiersURN: urn:nbn:se:liu:diva-71864OAI: oai:DiVA.org:liu-71864DiVA: diva2:454783