扩展卡尔曼滤波器
卡尔曼滤波器
估计员
控制理论(社会学)
协方差
计算机科学
不变扩展卡尔曼滤波器
容错
断层(地质)
数学
人工智能
统计
控制(管理)
分布式计算
地震学
地质学
作者
Yan Wang,Henglai Wei,Bin-Bin Hu,Chen Lv
出处
期刊:SAE International journal of vehicle dynamics, stability, and NVH
日期:2023-05-25
卷期号:7 (3)
被引量:9
标识
DOI:10.4271/10-07-03-0019
摘要
<div>The vehicle dynamic state is essential for stability control and decision-making of intelligent vehicles. However, these states cannot usually be measured directly and need to be obtained indirectly using additional estimation algorithms. Unfortunately, most of the existing estimation methods ignore the effect of data loss on estimation accuracy. Furthermore, high-order filters have been proven that can significantly improve estimation performance. Therefore, a second-order fault-tolerant extended Kalman filter (SOFTEKF) is designed to predict the vehicle state in the case of data loss. The loss of sensor data is described by a random discrete distribution. Then, an estimator of minimum estimation error covariance is derived based on the extended Kalman filter (EKF) framework. Finally, experimental tests demonstrate that the SOFTEKF can reduce the effect of data loss and improve estimation accuracy by at least 10.6% compared to the traditional EKF and fault-tolerant EKF.</div>
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