An improved nonlinear FastEuler AHRS estimation based on the SVDCKF algorithm
This work addresses attitude estimation for small UAVs, representing an incremental improvement over existing methods.
The paper tackled the problem of attitude estimation for small UAVs by proposing an improved nonlinear FastEuler AHRS model fused with a Singular Value Decomposition Cubature Kalman Filter (SVDCKF) algorithm, resulting in more excellent attitude solution accuracy compared to the CKF in low and high dynamic flight conditions.
In this paper, we present a Singular Value Decomposition Cubature Kalman Filter(SVDCKF) fusion algorithm based on the improved nonlinear FastEuler Attitude and Heading Reference and System(AHRS) estimation model for small-UAV attitude. The contributions of this work are the derivation of the low-cost IMU/MAG integrated AHRS model combined with the quaternion attitude determination, and use the FastEuler to correct the gyroscope attitude update, which can increase the real-time solution. In addition, the SVDCKF algorithm is fused the various raw sensors data in order to improve the filter accuracy compared with the CKF. The simulation and experiment results demonstrate the proposed algorithm has the more excellent attitude solution accuracy compared with the CKF in the low and high dynamic flight conditions.