An improved nonlinear FastEuler AHRS estimation based on the SVDCKF algorithm
Abstract
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.
Cite
@article{arxiv.2002.08317,
title = {An improved nonlinear FastEuler AHRS estimation based on the SVDCKF algorithm},
author = {Yue Yang},
journal= {arXiv preprint arXiv:2002.08317},
year = {2020}
}
Comments
arXiv admin note: substantial text overlap with arXiv:2002.03920