Research on Intelligent Vehicle Target State Tracking Based on Robust Adaptive SCKF

被引：0
作者：
Zhang Z. [1 ,2 ]
Zheng L. [1 ,2 ]
Li Y. [1 ,2 ]
Wu H. [1 ,2 ]
Yu Y. [1 ,2 ]
机构：
[1] College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing
[2] State Key Lab of Mechanical Transmissions, Chongqing University, Chongqing
来源：
Jixie Gongcheng Xuebao/Journal of Mechanical Engineering | 2021年 / 57卷 / 20期
关键词：
Intelligent vehicle; Robust adaptive; Square-root cubature Kalman filter; Target state tracking;
D O I：
10.3901/JME.2021.20.181
中图分类号：
学科分类号：
摘要：
Accurate estimation of the states of vehicle and front vehicle is the premise of the effective decision-making and control of the intelligent vehicle. However, previous studies usually do not consider the uncertainty of noise statistical characteristics, which leads to a large error of vehicle state estimation in some cases. Therefore, a robust adaptive square-root cubature Kalman filter (RASCKF) algorithm is proposed to reduce the influence of noise statistical uncertainty on estimation accuracy. Firstly, the statistical values of process noise covariance and measurement noise covariance are estimated by the maximum a posterior (MAP) criterion to improve the accuracy of state estimation when the noise is stable. Secondly, the fault detection rules are designed based on the standardized measurement innovation sequence, and the real-time measurement innovation is used to correct the noise covariances for ensure the robustness of the state estimation algorithm. Finally, the RASCKF algorithm is verified by simulation under different noise interference conditions. The results show that RASCKF algorithm is superior to standard SCKF algorithm in estimation accuracy and stability, which effectively solves the problem of uncertain noise statistical characteristics in the process of intelligent vehicle target state tracking. © 2021 Journal of Mechanical Engineering.
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页码：181 / 193
页数：12
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