Combining Extended Kalman Filter with Complementary Filter for UAV Attitude Estimation based on MEMS MARG Sensors
Xiang Ren, Shoubin Liu
Available Online January 2016.
- https://doi.org/10.2991/ifmeita-16.2016.136How to use a DOI?
- attitude estimation, data fusion, MEMS sensors, EKF, complementary filter.
- 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, gyrosâ€™ 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
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 - 2016 International Forum on Management, Education and Information Technology Application PB - Atlantis Press SN - 2352-5398 UR - https://doi.org/10.2991/ifmeita-16.2016.136 DO - https://doi.org/10.2991/ifmeita-16.2016.136 ID - Ren2016/01 ER -