Continuous self‐calibration of platform inertial navigation system based on attitude quaternion model

Abstract

This study focuses on the continuous self-calibration of platform inertial navigation system. Since the estimate accuracy of inertial platform error coefficients would be reduced by the large misalignment angle, the inertial platform gimbal angle is used to establish the continuous self-calibration model. And furthermore, to avoid a large number of triangle operations and the phenomenon of gimbal lock in traditional Euler angle represented gimbal angle continuous self-calibration model, an attitude quaternion continuous self-calibration model is proposed. In this model, the attitude quaternion is used to replace the Euler angle to describe the rotation of the inertial platform. Then, a quaternion unscented Kalman filter based on singular value decomposition (SVD-QUKF) is proposed to calibrate the error coefficients of the inertial platform in the attitude quaternion continuous self-calibration model. Finally, the simulation and experiment results prove that the proposed quaternion continuous self-calibration model and the SVD-QUKF can calibrate all the error coefficients of inertial platform with the relative error <1%, and the calibration precision and the computing speed are better than the traditional method.