Open AccessA Multi-sensor Data Fusion Algorithm Based on Unscented Kalman Filter for the Attitude Estimation of UAV
Author(s) -
Hongwei Wu,
Yusheng Dou,
Jianlong Liu
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1965/1/012001
Subject(s) - kalman filter , euler angles , extended kalman filter , control theory (sociology) , unscented transform , sensor fusion , algorithm , invariant extended kalman filter , computer science , attitude control , reliability (semiconductor) , inertial measurement unit , engineering , artificial intelligence , control engineering , control (management) , mathematics , power (physics) , physics , geometry , quantum mechanics
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).