TY - GEN
T1 - Spacecraft State Estimation with Multichannel Higher-order ARMA Colored Noises
AU - Zhang, Donglin
AU - Duan, Zhansheng
AU - Wang, Pengcheng
AU - Zhang, Yonghe
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - The celebrated Kalman filter (KF) is the workhorse and widely applied to many practical state estimation problems. It is optimal for linear systems with white noise. However, for systems with colored process and measurement noises, the KF loses its optimality and even diverges. In this paper, by modeling the colored noise as ARMA (auto-regressive moving average) model from its spectrum, two state estimators for systems with multichannel higher-order colored noises are proposed. One is state-augmented optimal filter (SAOF), and the other is measurement-differenced optimal one-step lag smoother (MDOLS). These two state estimators are both theoretically optimal in the sense of minimizing the mean square error among all linear state estimators. Illustrative examples demonstrate the effectiveness of the proposed state estimators.
AB - The celebrated Kalman filter (KF) is the workhorse and widely applied to many practical state estimation problems. It is optimal for linear systems with white noise. However, for systems with colored process and measurement noises, the KF loses its optimality and even diverges. In this paper, by modeling the colored noise as ARMA (auto-regressive moving average) model from its spectrum, two state estimators for systems with multichannel higher-order colored noises are proposed. One is state-augmented optimal filter (SAOF), and the other is measurement-differenced optimal one-step lag smoother (MDOLS). These two state estimators are both theoretically optimal in the sense of minimizing the mean square error among all linear state estimators. Illustrative examples demonstrate the effectiveness of the proposed state estimators.
KW - ARMA model
KW - Higher-order colored noise
KW - Multichannel noise
KW - Spacecraft state estimation
UR - https://www.scopus.com/pages/publications/85123996356
U2 - 10.1109/ICCAIS52680.2021.9624490
DO - 10.1109/ICCAIS52680.2021.9624490
M3 - 会议稿件
AN - SCOPUS:85123996356
T3 - 10th International Conference on Control, Automation and Information Sciences, ICCAIS 2021 - Proceedings
SP - 602
EP - 607
BT - 10th International Conference on Control, Automation and Information Sciences, ICCAIS 2021 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th International Conference on Control, Automation and Information Sciences, ICCAIS 2021
Y2 - 14 October 2021 through 17 October 2021
ER -