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Vehicle multistate estimation based on unscented particle filter algorithm


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Vehicle multistate estimation based on unscented particle filter algorithm
Lin FenZhao YouqunHuang Chao
College of Energy and Power Engineering,Nanjing University of Aeronautics and Astronautics, Nanjing 210016,China
vehicle dynamicsunscented particle filter algorithmvehiclesmultistatesstate estimationconstant noisePacejka tire modelunscented Kalman filter algorithmminimum meansquare errorparticle filter algorithm
Aiming at the problems of complicated calculation and low precision for common vehicle state estimation algorithms,a novel vehicle multistate estimation algorithm is proposed here.A 7 degrees of freedom(7DOF) nonlinear vehicle dynamic model containing constant noise and Pacejka tire model is established.Aiming at the defects of general particle filter(PF) algorithms,the unscented Kalman filter(UKF) algorithm is used to generate the importance density.The unscented particle filter(UPF) algorithm is used to realize the minimum meansquare error(MMSE) estimation of multiple key vehicle states.Estimators based on the UPF algorithm,UKF algorithm and PF algorithm are compared,and the results indicate the influences of numbers of particles on estimation accuracy.The results of a virtual experiment based on ADAMS/Car and a real vehicle experiment indicate that the accuracy of the estimator based on the UPF algorithm is higher than that of the estimator based on the UKF algorithm.The mean absolute errors of the estimate values of the estimator based on the UPF algorithm relative to the real values are lower than 10 percent of the modality amplitude.The realtime performance of the estimator based on the UPF algorithm is better than that of the estimator based on the PF algorithm.


[1]Ryu J H.State and parameter estimation for vehicle dynamics control using GPS[D].California,USA:Department of Mechanical Engineering,Stanford University,2005.
Lin Fen,Zhao Youqun.Comparison of methods for estimating vehicle side slip angle[J].Journal of Nanjing University of Science and Technology,2009,33(1):122-126,131.
Guo Konghui,Fu Hao,Ding Haitao.Estimation of CG sideslip angle based on extended Kalman filter[J].Automobile Technology,2009(4):1-3,44.
Wang Guolin,Fang Chao,Gao Hanwen.Estimation of vehicle attitude variables under large slip angle[J].Automobile Technology,2009(12):5-8.
Lin Fen,Zhao Youqun.Vehicle state and parameter estimation based on dual extended adaptive Kalman filter[J].China Mechanical Engineering,2009,20(6):750-755.
Zhao Youqun,Lin Fen.Vehicle state estimation based on unscented Kalman filter algorithm[J].China Mechanical Engineering,2010,21(5):615-619,629.
[7]Kiencke U,Nielsen L.Automotive control systems[M].Berlin,Germany:Springer,2000.
[8]Bakker E,Pacejka H B.A new tire model with an application in vehicle dynamics studies[EB/OL].http://papers.sae.org/890087/,1989-04-01.
[9]Arulampalam M S,Maskell S,Gordon N,et al.A tutorial on particle filters for online nonlinear/nonGaussian Bayesian tracking[J].IEEE Trans on Signal Processing,2002,50(2):174-188.
[10]Wan E A,Nelson A T.Kalman filtering and neural networks[M].New York,USA:John Wiley & Sons,2001.


Last Update: 2014-06-30