Quaternion derivative unscented Kalman filter-based two-step attitude estimation algorithm for multi-rotor unmanned aerial vehicle

被引：1
|
作者：
Cai A.-J. [1 ]
Liu K.-F. [1 ]
Guo S.-H. [2 ]
Shu Z. [1 ]
机构：
[1] College of Electromechanical Engineering, Xi'an University of Architecture and Technology, Xi'an, 710055, Shaanxi
[2] College of Civil Engineering, Xi'an University of Architecture and Technology, Xi'an, 710055, Shaanxi
来源：
Kongzhi Lilun Yu Yingyong/Control Theory and Applications | 2020年 / 37卷 / 02期
基金：
中国国家自然科学基金;
关键词：
Attitude estimation; Multi-rotor UAV; Quaternion; Unscented Kalman filter;
D O I：
10.7641/CTA.2019.80538
中图分类号：
学科分类号：
摘要：
The traditional attitude estimation algorithm for multi-rotor unmanned aerial vehicle (UAV) is difficult to balance high-precision, strong real-time and has poor anti-interference ability. To address this problem, a derivative unscented Kalman filter algorithm with a relatively small computational complexity is used firstly. In the measurement update, the acceleration data and magnetometer data are divided into two phases for attitude quaternion correction processing. Secondly, according to the nature of quaternion, the assumption that each element of the quaternion contains different attitude angle information is made. Finally, the calibration quaternion is multiplied by the coefficient designed to reduce the mutual interference in the calibration process. A quaternion derivative unscented Kalman filter-based two-step attitude estimation algorithm for multi-rotor unmanned aerial vehicle is proposed. The simulation comparison experiment between the proposed algorithm and the traditional attitude estimation algorithm is carried out by using PIXHAWK flight controller data. Experiments show that the proposed algorithm has great improvement in real-time performance, resolution accuracy and anti-interference ability compared with traditional attitude estimation algorithms using extended Kalman filter(EKF) or unscented Kalman filter (UKF). © 2020, Editorial Department of Control Theory & Applications. All right reserved.
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收藏
页码：365 / 373
页数：8
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