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An Adaptive Fault-Tolerant EKF for Vehicle State Estimation With Partial Missing Measurements
IEEE/ASME Transactions on Mechatronics ( IF 6.4 ) Pub Date : 2021-03-11 , DOI: 10.1109/tmech.2021.3065210
Yan Wang , Liwei Xu , Fengjiao Zhang , Haoxuan Dong , Ying Liu , Guodong Yin

Some vehicle state information that cannot be measured by in-vehicle sensors is quite important for the active safety control of intelligent vehicles. To obtain these key information in real-time, many advanced estimation algorithms are proposed. However, the existing studies focus on the effect of sensor measurement noise on estimation accuracy and rarely consider the impact of sensor data loss. In this article, a novel adaptive fault-tolerant extended Kalman filter is proposed to estimate vehicle state in case of partial loss of sensor data. The randomness of the data loss is first defined by a discreet distribution in interval [0,1]. Then, the fault-tolerant extended Kalman filter is derived based on a recursive filter framework. Furthermore, a fading factor on the basis of the orthogonal theory is used to improve the adaptability of fault-tolerant extended Kalman filter. Experimental results demonstrate that the estimation performance of the proposed approach is better than the extended Kalman filter.

中文翻译:

具有部分缺失测量的车辆状态估计的自适应容错 EKF

一些车载传感器无法测量的车辆状态信息对于智能车辆的主动安全控制非常重要。为了实时获取这些关键信息,提出了许多先进的估计算法。然而,现有的研究侧重于传感器测量噪声对估计精度的影响,很少考虑传感器数据丢失的影响。在本文中,提出了一种新颖的自适应容错扩展卡尔曼滤波器,用于在传感器数据部分丢失的情况下估计车辆状态。数据丢失的随机性首先由区间 [0,1] 中的离散分布定义。然后,基于递归滤波器框架推导出容错扩展卡尔曼滤波器。此外,基于正交理论的衰落因子被用来提高容错扩展卡尔曼滤波器的适应性。实验结果表明,该方法的估计性能优于扩展卡尔曼滤波器。
更新日期:2021-03-11
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