2012 Volume 6 Issue 5 Pages 754-766
An attitude sensor mounted on an unmanned system requires to have high accuracy in attitude estimation under a dynamic acceleration environment for autonomous attitude control. To estimate the attitude, an attitude sensor needs to detect the direction of gravity from measured accelerations, which include dynamic accelerations. However, accelerometers are usually sensitive to both gravity and dynamic accelerations. In this paper, two attitude estimation algorithms with a quaternion based on the extended Kalman filter are described and compared. One of the proposed algorithms uses a pre-filter. In this approach, measured accelerations are filtered by the pre-filter and classified into gravity and dynamic accelerations. Using only the measured gravitational accelerations, an EKF can estimate the attitude with high accuracy. In the other approach, the EKF includes a dynamical model of acceleration disturbance. It can estimate both the attitude and the acceleration disturbance. The comparative experiments with hand motion, moving vehicles, and UAVs, were carried out to evaluate these algorithms. In the result of pre-filter method, estimation errors were accumulated in long-term acceleration disturbance. Meanwhile, the EKF algorithm with a dynamical model of acceleration disturbance provided accurate results in each experiment. As a result, we concluded the latter method is well suited to a dynamic acceleration environment for unmanned systems.
