Attitude Heading Reference Algorithm Based On Transformed Cubature Kalman Filter

Stable and accurate attitude estimation is the key to the autonomous control of unmanned aerial vehicle. The Attitude Heading Reference System using micro-electro-mechanical system inertial measurement unit and magnetic sensor as measurement sensors is an indispensable system for attitude estimation of the unmanned aerial vehicle. Aiming at the problem of low precision of the Attitude Heading Reference System caused by the nonlinear attitude model of the micro unmanned aerial vehicle, an attitude heading reference algorithm based on cubature Kalman filter is proposed. Aiming at the nonlocal sampling problem of cubature Kalman filter, the transformed cubature Kalman filter using orthogonal transformation of the sampling point is presented. Meanwhile, an adaptive estimation algorithm of motion acceleration using Kalman filter is proposed, which realizes the online estimation of motion acceleration. The car-based tests show that the algorithm proposed in this paper can accurately estimate the carrier's motion attitude and motion acceleration without global positioning system. The accuracy of acceleration reaches 0.2 m/s(2), and the accuracy of attitude reaches 1 degrees.