Combining Extended Kalman Filter with Complementary Filter for UAV Attitude Estimation based on MEMS MARG Sensors

DOI

10.2991/ifmeita-16.2016.136How to use a DOI?

Keywords

attitude estimation, data fusion, MEMS sensors, EKF, complementary filter.

Abstract

Reliable attitude information is desired for navigation and control of rotor unmanned aerial vehicles (UAV). Rotor UAV's attitude can be determined by fusing redundant data from MEMS MARG (Magnetic, Angular Rate, and Gravity) sensors with fusion techniques. The extended Kalman filter (EKF)-based fusion algorithms are commonly adopted. However, there exists a contradiction between convergence speed and noise suppression in EKF-based algorithms. This paper presents a novel fusion algorithm combining EKF with complementary filter to estimate the attitude of rotor UAV. Firstly, gyros’ measurements of angular rates are corrected by measurements of accelerometers with a Mahony passive complementary filter. The corrected angular rates as well as the measurements of accelerometers and magnetometers are then input to an EKF to implement data fusion. Results of validation experiments show that the proposed fusion method can generate attitude angles accurately and fuse multi-sensor data efficiently.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Xiang Ren
AU  - Shoubin Liu
PY  - 2016/01
DA  - 2016/01
TI  - Combining Extended Kalman Filter with Complementary Filter for UAV Attitude Estimation based on MEMS MARG Sensors
BT  - Proceedings of the 2016 International Forum on Management, Education and Information Technology Application
PB  - Atlantis Press
SP  - 746
EP  - 752
SN  - 2352-5398
UR  - https://doi.org/10.2991/ifmeita-16.2016.136
DO  - 10.2991/ifmeita-16.2016.136
ID  - Ren2016/01
ER  -