Application of Federal Kalman Filter with Neural Networks in the Velocity and Attitude Matching of Transfer Alignment
Joint Authors
Song, Lijun
Duan, Zhongxing
He, Bo
Li, Zhe
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-01-21
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
The centralized Kalman filter is always applied in the velocity and attitude matching of Transfer Alignment (TA).
But the centralized Kalman has many disadvantages, such as large amount of calculation, poor real-time performance, and low reliability.
In the paper, the federal Kalman filter (FKF) based on neural networks is used in the velocity and attitude matching of TA, the Kalman filter is adjusted by the neural networks in the two subfilters, the federal filter is used to fuse the information of the two subfilters, and the global suboptimal state estimation is obtained.
The result of simulation shows that the federal Kalman filter based on neural networks is better in estimating the initial attitude misalignment angle of inertial navigation system (INS) when the system dynamic model and noise statistics characteristics of inertial navigation system are unclear, and the estimation error is smaller and the accuracy is higher.
American Psychological Association (APA)
Song, Lijun& Duan, Zhongxing& He, Bo& Li, Zhe. 2018. Application of Federal Kalman Filter with Neural Networks in the Velocity and Attitude Matching of Transfer Alignment. Complexity،Vol. 2018, no. 2018, pp.1-7.
https://search.emarefa.net/detail/BIM-1133480
Modern Language Association (MLA)
Song, Lijun…[et al.]. Application of Federal Kalman Filter with Neural Networks in the Velocity and Attitude Matching of Transfer Alignment. Complexity No. 2018 (2018), pp.1-7.
https://search.emarefa.net/detail/BIM-1133480
American Medical Association (AMA)
Song, Lijun& Duan, Zhongxing& He, Bo& Li, Zhe. Application of Federal Kalman Filter with Neural Networks in the Velocity and Attitude Matching of Transfer Alignment. Complexity. 2018. Vol. 2018, no. 2018, pp.1-7.
https://search.emarefa.net/detail/BIM-1133480
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1133480