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基于 U KF 算法的汽车状态估计———赵又群林棻
基于 U KF 算法的汽车状态估计
赵又群林棻
南京航空航天大学,南京,210016
摘要:准确实时获取行驶过程中的状态信息是汽车动态控制系统研究的关键问题。将 unscented
卡尔曼滤波(U KF) 算法应用到汽车的状态估计之中,建立了包含时不变统计特性噪声和非线性轮胎的
汽车动力学模型,采用具有对称采样策略和比例修正的 U KF 算法对汽车估计了多个关键状态量。将
U KF 估计器与常见的 E KF 估计器进行了比较分析,基于 ADAMS/ Car 的虚拟试验和实车试验验证了
U KF 在汽车状态估计中的可行性。
关键词:汽车动力学;unscented 卡尔曼滤波(U KF) ;状态估计;虚拟试验
中图分类号:U461. 6 文章编号:1004 —132X(2010) 05 —0615 —05
Vehicle State Estimation Based on Unscented Kalman Filter Algorithm
Zhao Youqun Lin Fen
Nanjing University of Aeronautics & Astronautics ,Nanjing ,210016
Abstract : A ponent of vehicle dynamic control systems is as accurate and real time
knowledge of vehicleΟkey states when running on road. U KF algorit hm was used in vehicle state esti
mation. The nonlinear vehicle dynamics system which contained constant noise and nonlinear tire
model was established. Several vehicle key states were estimated using U KF with symmetrical sam
pling strategy and proportional correction. The estimator based on U KF pared wit h t he estima
tor based on extended Kalman filter ( E KF) . The result s of virtual experiment s based on ADAMS/ Car
and real vehicle experiment s demonstrate t hat U KF is available in vehicle state estimation.
Key words : vehicle dynamics ; unscented Kalman filter ( U KF) ; state estimation ; virt ual experi
ment
0 引言在运算时需要求解繁琐的 J acobian 矩阵,不但容
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