An Adaptive Fault-Tolerant EKF for Vehicle State Estimation With Partial Missing Measurements

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.