Open AccessQuaternion-Based Kalman Filter for AHRS Using an Adaptive-Step Gradient Descent Algorithm
Author(s) -
Li Wang,
Zhang Zheng,
Ping Sun
Publication year - 2015
Publication title -
international journal of advanced robotic systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.394
H-Index - 46
eISSN - 1729-8814
pISSN - 1729-8806
DOI - 10.5772/61313
Subject(s) - attitude and heading reference system , quaternion , euler angles , control theory (sociology) , kalman filter , accelerometer , computer science , orientation (vector space) , gyroscope , invariant extended kalman filter , extended kalman filter , rotation (mathematics) , algorithm , mathematics , computer vision , artificial intelligence , engineering , geometry , control (management) , operating system , aerospace engineering
This paper presents a quaternion-based Kalman filter for real-time estimation of the orientation of a quadrotor. Quaternions are used to represent rotation relationship between navigation frame and body frame. Processing of a 3-axis accelerometer using Adaptive-Step Gradient Descent (ASGD) produces a computed quaternion input to the Kalman filter. The step-size in GD is set in direct proportion to the physical orientation rate. Kalman filter combines 3-axis gyroscope and computed quaternion to determine pitch and roll angles. This combination overcomes linearization error of the measurement equations and reduces the calculation cost. 3-axis magnetometer is separated from ASGD to independently calculate yaw angle for Attitude Heading Reference System (AHRS). This AHRS algorithm is able to remove the magnetic distortion impact. Experiments are carried out in the small-size flight controller and the real world flying test shows the proposed AHRS algorithm is adequate for the real-time estimation of the orientation of a quadrotor