[1]林棻,赵又群,黄超.基于UPF算法的汽车多状态量估计[J].南京理工大学学报(自然科学版),2014,38(03):402.
 Lin Fen,Zhao Youqun,Huang Chao.Vehicle multistate estimation based on unscented particle filter algorithm[J].Journal of Nanjing University of Science and Technology,2014,38(03):402.
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基于UPF算法的汽车多状态量估计
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《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

卷:
38卷
期数:
2014年03期
页码:
402
栏目:
出版日期:
2014-06-30

文章信息/Info

Title:
Vehicle multistate estimation based on unscented particle filter algorithm
作者:
林棻赵又群黄超
南京航空航天大学 能源与动力学院,江苏 南京 210016
Author(s):
Lin FenZhao YouqunHuang Chao
College of Energy and Power Engineering,Nanjing University of Aeronautics and Astronautics, Nanjing 210016,China
关键词:
汽车动力学非追踪粒子滤波算法汽车多状态量状态估计定常统计特性噪声Pacejka轮胎模型非追踪卡尔曼滤波算法最小均方误差粒子滤波算法
Keywords:
vehicle dynamicsunscented particle filter algorithmvehiclesmultistatesstate estimationconstant noisePacejka tire modelunscented Kalman filter algorithmminimum meansquare errorparticle filter algorithm
分类号:
U461.6
摘要:
针对常用汽车状态估计算法计算复杂、精度低等问题,提出一种新的汽车多状态量估计方法。建立了包含定常统计特性噪声和Pacejka轮胎模型的七自由度非线性汽车动力学模型。针对一般粒子滤波(PF)算法存在的缺陷,使用非追踪卡尔曼滤波(UKF)算法产生粒子滤波的重要性概率密度。基于非追踪粒子滤波(UPF)算法实现对汽车多个关键状态量的最小均方误差估计。将基于UPF算法、UKF算法与PF算法的估计器进行了比较,揭示了粒子数对汽车状态估计效果的影响。基于ADAMS/Car的虚拟实验和实车实验表明基于UPF算法的估计器精
Abstract:
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.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2012-05-14修回日期:2012-08-10
基金项目:国家自然科学基金(10902049);中国博士后科学基金(2012M521073);江苏省博士后基金(1302020C)
作者简介:林棻(1980-),男,博士,副教授,主要研究方向:汽车动力学与控制,E-mail:flin@nuaa.edu.cn。
引文格式:林棻,赵又群,黄超.基于UPF算法的汽车多状态量估计[J].南京理工大学学报,2014,38(3):402-408.
投稿网址:http://zrxuebao.njust.edu.cn
更新日期/Last Update: 2014-06-30