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Accurate Attitude Determination Based on Adaptive UKF and RBF Neural Network Using Fusion Methodology for Micro-IMU Applied to Rotating Environment
Joint Authors
Wang, Lei
Meng, Zhi Min
Guan, Ying
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-17, 17 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-07-31
Country of Publication
Egypt
No. of Pages
17
Main Subjects
Abstract EN
Focusing on the issue of attitude tracking for low-cost and small-size Micro-Electro-Mechanical System (MEMS) Inertial Measurement Unit (IMU) in high dynamic environment, an Adaptive Unscented Kalman Filter (AUKF) method combining sensor fusion methodology with Artificial Neural Network (ANN) is proposed.
The different control strategies are adopted by fusing multi-MEMS inertial sensors under various dynamic situations.
The AUKF attitude determination approach utilizing the MEMS sensor and Global Positioning System (GPS) can provide reliable estimation in these situations.
In particular, the adaptive scale factor is used to adaptively weaken or enhance the effects on new measurement data according to the predicted residual vector in the estimation process.
In order to solve the problem that the new measurement data is not available in case of GPS fault, an attitude algorithm based on Radial Basis Function (RBF)-ANN feedback correction is proposed for AUKF.
The estimated deviation of predicted system state can be provided based on RBF-ANN in GPS-denied environment.
The corrected predicted system state is used for the estimation process in AUKF.
An experimental platform was setup to simulate the rotation of the spinning projectile.
The experimental results show that the proposed method has better performance in terms of attitude estimation than other representative methods under various dynamic situations.
American Psychological Association (APA)
Wang, Lei& Meng, Zhi Min& Guan, Ying. 2020. Accurate Attitude Determination Based on Adaptive UKF and RBF Neural Network Using Fusion Methodology for Micro-IMU Applied to Rotating Environment. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-17.
https://search.emarefa.net/detail/BIM-1193362
Modern Language Association (MLA)
Wang, Lei…[et al.]. Accurate Attitude Determination Based on Adaptive UKF and RBF Neural Network Using Fusion Methodology for Micro-IMU Applied to Rotating Environment. Mathematical Problems in Engineering No. 2020 (2020), pp.1-17.
https://search.emarefa.net/detail/BIM-1193362
American Medical Association (AMA)
Wang, Lei& Meng, Zhi Min& Guan, Ying. Accurate Attitude Determination Based on Adaptive UKF and RBF Neural Network Using Fusion Methodology for Micro-IMU Applied to Rotating Environment. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-17.
https://search.emarefa.net/detail/BIM-1193362
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1193362