A fast robust Kalman filter for initial alignment of strapdown inertial navigation system

This paper investigates the non-Gaussian initial alignment for the strapdown inertial navigation system (SINS) with main focus on reducing the computational burden, meanwhile, ensuring the robustness of alignment filter. Conventional Kalman filter (KF) is the commonly used alignment filter, which brings a large computational burden during the filtering process, and assumes the statistic characteristics of the system noise is Gaussian in advance. When there are non-Gaussian characteristics in practice, it is difficult to improve the alignment filter speed without sacrificing the filter accuracy. In view of this problem, a fast robust Kalman filter (FRKF) is proposed. The approximated matrix inversion based on Neumann series expansion is used in the update step of the maximum correntropy criterion KF (MCC-KF). The computational complexity of the KF/MCCKF/FRKF is analyzed, the approximated inversion method requires much less computational cost than the traditional inversion method, such that the FRKF is attractive from a complexity point of view. The alignment results show that the FRKF can obtain the state estimation accuracy as well as MCC-KF, while the computational burden is reduced remarkably.