摘要
为准确实时获取汽车行驶过程中的状态变量,提出了一种模型自适应更新粒子滤波方法。建立了非高斯噪声和非线性轮胎的汽车动力学模型,并基于小波变换的方法,采用高频子带估计传感器量测噪声的实时方差,提高了观测似然函数的真实拟合程度,结合自适应自回归模型对整车系统的状态进行自适应更新,较好地克服了粒子权值的退化现象;基于ADAMS/Car的虚拟实验和实车实验验证了所提方法的有效性。实验结果表明该方法在估计精度和克服噪声方面均优于常用方法,满足汽车状态估计器的软件性能要求。
In order to get the accurate and real-time vehicle state variables in running,a new kind of model adaptive update particle filter method is proposed.The non-Gaussian and non-linear tire noise vehicle dynamics model is established.High frequency sub-band is used to estimate real-time measurement noise variance of sensors based on the wavelet transform.The real fitting degree of observation likelihood function is improved and the degradation phenomenon of particle weight is improved to a certain extent by the combination of the adaptive auto regression model of the whole vehicle system state.Virtual experiment based on ADAMS /Car and real vehicle experiment verify the validity of the proposed method.Experiment results show that the estimation precision and anti-noise performance of the proposed method are superior to those of the commonly used method,and can satisfy the requirements of vehicle state estimation.
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2014年第10期22-28,共7页
Transactions of the Chinese Society for Agricultural Machinery
基金
国家自然科学基金资助项目(61263031)
江苏省大型工程装备检测与控制重点建设实验室重点资助项目(JSKLEDC201202)
关键词
汽车动力学
状态估计
模型自适应
粒子滤波
Vehicle dynamics State estimation Model adaptive Particle filter
