Self-Calibration of Tri-Axis Rotational Inertial Navigation System Based on Virtual Platform

Abstract

There exist two structures of tri-axis rotational inertial navigation systems (TRINSs), namely, the outer-azimuth and inner-azimuth systems. Accurate calibration is one of the key endeavors toward high-precision TRINS. Regarding the TRINS calibration, there is a strict requirement of the navigation platform definition for the rotation excitation sequence. It means that one rotation excitation sequence cannot be applied to both outer-azimuth and inner-azimuth TRINSs. In this article, a virtual platform is constructed that is different from the usual navigation platform, the virtual platform is obtained by performing specific orthogonal transformation according to the requirements of the application scenario: one is to perform an orthogonal transformation on the $g$ -frame and $a$ -frame, and the other is to perform an orthogonal transformation on the $p$ -frame. Based on the self-calibration rotation excitation sequence of the inner-azimuth TRINS, the Kalman filtering is employed to estimate the error parameters in the virtual platform that are then used to calibrate the outer-azimuth TRINS. Real tests with an outer-azimuth TRINS show that the proposed calibration method achieves the scale-factor accuracy of 0.51 ppm and installation accuracy of $0.11''$ for gyroscopes, and the scale-factor accuracy of 1.56 ppm, and the installation accuracy of $0.17''$ for accelerometers, the proposed method can improve the observability of self-calibration.