考虑噪声和初始状态不确定性的车辆状态UKF估计

doi:10.3969/j.issn.1674-8484.2025.03.007

摘要/Abstract

摘要：

为提高车辆在噪声协方差矩阵和初始状态存在不确定性时的状态估计精度，提出了改进无迹Kalman滤波（UKF）车辆状态估计方法。该方法基于最大后验概率估计（MAP）策略引入加窗处理，实现对噪声协方差矩阵的动态估计；同时结合静态粒子滤波（SPF）算法，对车辆初始状态进行估计。利用CarSim与MATLAB/Simulink的联合仿真平台，对改进UKF的车辆状态估计精度进行验证。结果表明：在量测噪声偏离真实值的情况下，采用加窗MAP噪声协方差矩阵动态估计方法相比标准UKF，纵向与横向车速的估计精度分别提升了90%和80%；与噪声协方差矩阵自适应调整的UKF相比，估计精度分别提高了75%和56%。在初始状态不确定的情况下，SPF方法分别提高了纵向和横向车速的估计精度为94%和90%。因此提出的改进UKF估计方法在噪声协方差矩阵和初始状态存在不确定性时，显著提升了估计精度和鲁棒性。

关键词: 电动汽车, 车辆状态估计, 无迹Kalman滤波（UKF）, 最大后验概率估计（MAP）, 静态粒子滤波（SPF）

Abstract:

An improved unscented Kalman filter (UKF) vehicle state estimation method was proposed to improve the estimation accuracy of vehicle states in the presence of noise covariance matrix and initial state uncertainties. This method introduced a windowing process based on the maximum a posteriori (MAP) estimation strategy to achieve dynamic estimation of the noise covariance matrix, while also integrating a static particle filter (SPF) algorithm to estimate the initial vehicle states. The improved UKF's estimation accuracy was verified using a co-simulation platform with CarSim and MATLAB/Simulink. The results show that, when measurement noise deviates from the true value, the windowed MAP dynamic estimation method for the noise covariance matrix improves the estimation accuracy of longitudinal and lateral speeds by 90% and 80%, respectively, compared to the standard UKF. In comparison to the UKF with adaptive noise covariance matrix adjustment, the estimation accuracy increases by 75% and 56%, respectively. Under initial state uncertainty, the SPF method improves the estimation accuracy of longitudinal and lateral vehicle speeds by 94% and 90%, respectively. Therefore, the proposed improved UKF estimation method significantly enhances estimation accuracy and robustness in the presence of noise covariance matrix and initial state uncertainties.

Key words: electric vehicle, vehicle state estimation, unscented Kalman filter (UKF), maximum a posteriori (MAP), static particle filter (SPF)

中图分类号:

U461.6

张志勇, 杜宸胄, 易晟, 于辉. 考虑噪声和初始状态不确定性的车辆状态UKF估计[J]. 汽车安全与节能学报, 2025, 16(3): 414-424.

ZHANG Zhiyong, DU Chenzhou, YI Sheng, YU Hui. Vehicle state UKF estimation considering noise and initial state uncertainties[J]. Journal of Automotive Safety and Energy, 2025, 16(3): 414-424.

图/表 13

图1 三自由度车辆动力学模型

图2 MAP窗口移动过程

图3 车辆状态精度验证方案

图4 前轮转角

整车质量，m	1 530 kg
横摆转动惯量，I_z	4 607 kg·m²
前轴到质心距离，a	1.11 m
后轴到质心距离，b	1.79 m
轮距，t_w	1.55 m

表1 车辆参数

图5 估计精度对比(R0 = 0.01×eye(3))

图6 估计误差对比(R0 = 0.01×eye(3))

图7 估计精度对比(R0 = 0.5×eye(3))

图8 估计误差对比(R0 = 0.5×eye(3))

R初值	车辆状态	SUKF	NAA-UKF	WMAP-UKF
0.01×eye(3)	纵向车速	0.092 3	0.105 0	0.110 0
0.01×eye(3)	横向车速	0.064 9	0.066 6	0.062 9
0.5×eye(3)	纵向车速	1.090 1	0.439 1	0.108 2
0.5×eye(3)	横向车速	0.317 8	0.142 2	0.062 1

表2 估计误差均方根值

图9 初始状态不确定估计精度对比

图10 初始状态不确定估计误差对比

车辆状态	WMAP-UKF	SPF-WMAP-UKF
纵向车速	2.676 8	0.149 2
横向车速	1.115 2	0.114 2

表3 初始状态不确定估计误差均方根值

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