基于模型自适应粒子滤波的汽车状态估计

Estimation of Vehicle States Based on Adaptive Model Particle Filter

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。

Abstract:

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.