Study on Information Fusion Algorithm for the Miniature AHRS

Abstract

In this paper, a low-cost micro attitude and heading measurement system using MEMS inertial sensors is researched. To overcome shortcomings such as low precision and easy divergence, a new Kalman filter algorithm based on additive quaternion is designed. The state equation is established which taking attitude quaternion error and gyro drift as state variables. The measurement equation is constructed taking the attitude quaternion among accelerometers, magnetometers and gyroscopes. The stimulation indicates that the output of the AHRS is stable and within reasonable accuracy. Thus, the particular Kalman filter based on the additive quaternion error model is a practical method for improving the attitude and heading angles estimates.