Design and Implementation of an AHRS Based on Gauss Newton and Complementary Filtering Algorithm

Abstract

This paper presents design and implementation of an attitude and heading reference system (AHRS) based on Gauss Newton and Complementary Filtering algorithm (CF). The algorithm uses data measured from the MEMS sensor which contains a three-axis magnetometer, a three-axis angular rate sensor, and a three-axis accelerometer. The filter represents rotations using quaternions rather than Euler angles, which eliminates the long-standing problem of singularities associated with attitude estimation. A process model for rigid body angular motions and angular rate measurements is defined. The process model converts angular rates into quaternion rates, which are integrated to obtain quaternions. The Gauss-Newton iteration algorithm is utilized to find the optimal quaternion that relates the measured accelerations and earth magnetic field in the body coordinate frame to calculated values in the earth coordinate frame. Then fuse the optimal quaternion with the quaternion updated from gyroscope and calculate the attitude angle based on the complementary filtering algorithm . Extensive testing of the filter have proved feasibility and acceptable performance of this AHRS design.