TY - GEN
T1 - An Adaptive Extended Kalman Filter for Attitude Estimation Using Low-Cost IMU from Motor Vibration Disturbance
AU - Xu, Zhenduo
AU - Tian, Junxi
AU - Chao, Tao
AU - Yang, Ming
AU - Fang, Ke
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2023
Y1 - 2023
N2 - In the challenging environment of global navigation satellite systems (GNSS), in order to improve the accuracy of navigation, it is important to improve the accuracy of attitude calculation, which is significantly affected by various sensor noises and attitude calculation methods. The noise of sensor output will become larger with the increase of motor speed. In this paper, four algorithms including complementary filter method (CF), gradient descent algorithm (GDA), and extended Kalman filter method (EKF) are introduced for attitude estimation when the motor speed changes. Besides, in order to resist the influence of motor vibration, an adaptive factor is introduced into the EKF for attitude estimation. The result shows that the average value of adaptive extended Kalman filter (AEKF) solution error is about 0.13° and the variance is about 0.115°, which is greatly reduced compared with the other three methods.
AB - In the challenging environment of global navigation satellite systems (GNSS), in order to improve the accuracy of navigation, it is important to improve the accuracy of attitude calculation, which is significantly affected by various sensor noises and attitude calculation methods. The noise of sensor output will become larger with the increase of motor speed. In this paper, four algorithms including complementary filter method (CF), gradient descent algorithm (GDA), and extended Kalman filter method (EKF) are introduced for attitude estimation when the motor speed changes. Besides, in order to resist the influence of motor vibration, an adaptive factor is introduced into the EKF for attitude estimation. The result shows that the average value of adaptive extended Kalman filter (AEKF) solution error is about 0.13° and the variance is about 0.115°, which is greatly reduced compared with the other three methods.
KW - Adaptive extended Kalman Filter (AEKF)
KW - Motor vibration
KW - Quaternion
KW - Unmanned aerial vehicle (UAV)
UR - https://www.scopus.com/pages/publications/85151127162
U2 - 10.1007/978-981-19-6613-2_305
DO - 10.1007/978-981-19-6613-2_305
M3 - 会议稿件
AN - SCOPUS:85151127162
SN - 9789811966125
T3 - Lecture Notes in Electrical Engineering
SP - 3140
EP - 3148
BT - Advances in Guidance, Navigation and Control - Proceedings of 2022 International Conference on Guidance, Navigation and Control
A2 - Yan, Liang
A2 - Duan, Haibin
A2 - Deng, Yimin
A2 - Yan, Liang
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Conference on Guidance, Navigation and Control, ICGNC 2022
Y2 - 5 August 2022 through 7 August 2022
ER -