TY - GEN
T1 - Optimized Kalman Filter Approach with Innovation-based Outlier Diagnosis
AU - Ge, Baoshuang
AU - Zhang, Hai
AU - Sheng, Wei
AU - Chen, Jieling
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/8
Y1 - 2018/8
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85082444607
U2 - 10.1109/GNCC42960.2018.9018724
DO - 10.1109/GNCC42960.2018.9018724
M3 - 会议稿件
AN - SCOPUS:85082444607
T3 - 2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018
BT - 2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018
Y2 - 10 August 2018 through 12 August 2018
ER -