Randomly Weighted CKF for Multisensor Integrated Systems
Joint Authors
Zong, Hua
Gao, Zhaohui
Wei, Wenhui
Zhong, Yongmin
Gu, Chengfan
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-19, 19 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-11-11
Country of Publication
Egypt
No. of Pages
19
Main Subjects
Abstract EN
The cubature Kalman filter (CKF) is an estimation method for nonlinear Gaussian systems.
However, its filtering solution is affected by system error, leading to biased or diverged system state estimation.
This paper proposes a randomly weighted CKF (RWCKF) to handle the CKF limitation.
This method incorporates random weights in CKF to restrain system error’s influence on system state estimation by dynamic modification of cubature point weights.
Randomly weighted theories are established to estimate predicted system state and system measurement as well as their covariances.
Simulation and experimental results as well as comparison analyses demonstrate the presented RWCKF conquers the CKF problem, leading to enhanced accuracy for system state estimation.
American Psychological Association (APA)
Zong, Hua& Gao, Zhaohui& Wei, Wenhui& Zhong, Yongmin& Gu, Chengfan. 2019. Randomly Weighted CKF for Multisensor Integrated Systems. Journal of Sensors،Vol. 2019, no. 2019, pp.1-19.
https://search.emarefa.net/detail/BIM-1187174
Modern Language Association (MLA)
Zong, Hua…[et al.]. Randomly Weighted CKF for Multisensor Integrated Systems. Journal of Sensors No. 2019 (2019), pp.1-19.
https://search.emarefa.net/detail/BIM-1187174
American Medical Association (AMA)
Zong, Hua& Gao, Zhaohui& Wei, Wenhui& Zhong, Yongmin& Gu, Chengfan. Randomly Weighted CKF for Multisensor Integrated Systems. Journal of Sensors. 2019. Vol. 2019, no. 2019, pp.1-19.
https://search.emarefa.net/detail/BIM-1187174
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1187174