A Multi-sensor Data Fusion Algorithm Based on Unscented Kalman Filter for the Attitude Estimation of UAV

Aiming at the low accuracy of the inertial measurement unit and the error of the traditional attitude estimation algorithm, a UAV attitude estimation algorithm based on Unscented Kalman Filter (UKF) is proposed. The Euler angle method is used to describe the attitude algorithm model of the aircraft,...

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Veröffentlicht in:Journal of physics. Conference series Jg. 1965; H. 1; S. 12001 - 12006
Hauptverfasser: Wu, Hongwei, Dou, Yinke, Liu, Jianlong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Bristol IOP Publishing 01.07.2021
Schlagworte:
Aircraft
Algorithms
Attitudes
Control data (computers)
Data integration
Equations of state
Error analysis
Euler angles
Extended Kalman filter
Flight control
Inertial platforms
Multisensor fusion
Unmanned aerial vehicles
ISSN:1742-6588, 1742-6596
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Zusammenfassung:Aiming at the low accuracy of the inertial measurement unit and the error of the traditional attitude estimation algorithm, a UAV attitude estimation algorithm based on Unscented Kalman Filter (UKF) is proposed. The Euler angle method is used to describe the attitude algorithm model of the aircraft, and on this basis, the system state equation and observation equation of the UAV are established; the unscented Kalman filter algorithm is used to achieve the calculation of the attitude angle of the aircraft. By using APM flight control data, the simulation experiment is compared with the traditional attitude estimation algorithm. The experimental results show that the proposed algorithm has a great improvement in reliability and accuracy compared with the attitude estimation algorithm using extended Kalman filter (EKF).
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SourceType-Scholarly Journals-1
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ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1965/1/012001