Due to the statistical property of measurement noise varying from time and outliers in engineering applications, the standard Kalman filter is oscillating or even divergent. To solve this problem, a new optimal method is proposed. The measurement covariance is estimated more precisely in time by a replacement of a posteriori covariance at last step with a priori covariance which contains more current information. A novel three-segment function allowing to simultaneously restrain the outliers and tune the a posteriori covariance is presented. The experimental results show that the proposed method outperforms the common robust adaptive filter.
Optimized Kalman Filter Approach with Innovation-based Outlier Diagnosis
2018-08-01
369400 byte
Conference paper
Electronic Resource
English
Kalman Filter Partial innovation Sequence Monitor
TIBKAT | 2022
|Kalman Filter Partial Innovation Sequence Monitor
British Library Conference Proceedings | 2022
|