Inner Attitude Integration Algorithm Based on Fault Detection for Strapdown Inertial Attitude and Heading Reference System

This article proposes a new inner attitude integration algorithm to improve attitude accuracy of the strapdown inertial attitude and heading reference system(SIAHRS), which, by means of a Kalman filter, integrates the calculated attitude from the accelerometers in inertial measuring unit(IMU), called damping attitudes, with those from the conventional IMU. As vehicleʹs acceleration could produce damping attitude errors, the horizontal outputs from accelerometers are firstly used to judge the vehicleʹs motion so as to determine whether the damping attitudes could be reasonably applied. This article also analyzes the limitation of this approach. Furthermore, it suggests a residual chi-square test to judge the validity of damping attitude measurement in real time, and accordingly puts forward proper information fusion strategy. Finally, the effectiveness of the proposed algorithm is proved through the experiments on a real system in dynamic and static states.