改进自适应无迹卡尔曼滤波车辆状态估计应用  

Application of Enhanced Adaptive Unscented Kalman Filter for Vehicle State Estimation

作　　者：郜建行 钟勇 曹龙龙 范周慧 GAO Jianhang;ZHONG Yong;CAO Longlong;FAN Zhouhui(Key Laboratory of Automotive Electronics and Electric Drive Technology,Fujian University of Science and Technology,Fuzhou 350118,China)

机构地区：[1]福建理工大学汽车电子与电驱动技术重点实验室,福州350118

出　　处：《机电技术》2025年第1期69-75,共7页Mechanical & Electrical Technology

摘　　要：针对车辆状态估计中过程噪声和量测噪声统计特性不确定的问题,文章基于改进自适应无迹卡尔曼滤波(IAUKF)算法提出了一种车辆状态估计方法。该算法通过引入强跟踪滤波原理,对AUKF进行优化,以间接量测更新的方式降低计算复杂度,并自适应地调整噪声及其协方差。该算法提高了滤波器在复杂动态环境中的稳定性和估计精度,尤其在处理模型和噪声不确定性时,增强了目标状态的跟踪能力。使用Carsim与Matlab/Simulink进行联合仿真来验证IAUKF的有效性,并与无迹卡尔曼滤波(UKF)算法、自适应无迹卡尔曼滤波(AUKF)算法进行对比。仿真结果表明:相较其他算法IAUKF算法在车辆状态估计中表现更好,能以更高精度估计车辆的纵向车速、质心侧偏角和横摆角速度,有效提升了车辆状态参数估计的准确性、稳定性和抗干扰性。To address the uncertainty in statistical characteristics of process noise and measurement noise in vehicle state esti-mation,the article proposes a vehicle state estimation method based on an improved adaptive unscented Kalman filter(IAUKF)algorithm.By incorporating the strong tracking filtering principle,the algorithm optimizes the AUKF through indirect measurement updates to reduce computational complexity while adaptively adjusting noise and its covariance.The proposed method enhances the stability and estimation accuracy of the filter in complex dynamic environments,particularly improving target state tracking ca-pability when handling model and noise uncertainties.Co-simulation using Carsim and MATLAB/Simulink was conducted to vali-date the effectiveness of IAUKF,with comparative analyses against the unscented Kalman filter(UKF)and adaptive unscented Kalman filter(AUKF)algorithms.Simulation results demonstrate that the IAUKF algorithm outperforms other methods in vehicle state estimation,achieving higher precision in estimating longitudinal velocity,sideslip angle,and yaw rate.This approach signifi-cantly improves the accuracy,stability,and anti-interference capability of vehicle state parameter estimation.

关 键 词：改进自适应无迹卡尔曼滤波 车辆状态估计 联合仿真 抗干扰性 

分 类 号：U461.1[机械工程—车辆工程]