Performance improvement of in-flight alignment for autonomous vehicle under large initial heading error

The paper presents a new method of in-flight alignment (IFA) for a strapdown inertial navigation system (SDINS) aided by a super short baseline (SSBL) system under a large initial heading error. To handle the large heading error, a new attitude error model is introduced in which attitude errors are divided into heading and levelling errors using a newly defined horizontal frame. Some navigation error dynamic models are derived from the attitude error model for indirect feedback filtering of the IFA system. A Kalman filter with position measurements is designed to estimate navigation errors as an indirect feedback filter. Simulation results show that the newly developed model works effectively for coarse alignment of a large heading error. The proposed IFA method very quickly decreases the heading error from more than 40° to about 5°.