Adaptive transformed Cubature Kalman filter and its application in initial alignment of Strapdown Inertial Navigation System

Abstract For the strong nonlinearity of initial alignment with large misalignment angles of Strapdown Inertial Navigation System (SINS), improving the accuracy of the nonlinear filtering algorithm is important for navigation. In order to solve the non-local sampling problem of Cubature Kalman Filter (CKF) algorithm when the state dimension increases, the orthogonal transformation of sigma points is introduced to reduce the high-order component errors of the first moment of CKF. A short-term adaptive algorithm with better robustness is proposed on the basis of adaptive filtering algorithm and Transformed CKF (TCKF), also making better convergence efficiency and accuracy of initial alignment with large misalignment angles. For the application of non-linear initial alignment, the influence of the sub-optimal algorithm of adaptive filter on the stability of alignment is reduced by controlling the number of effective filtering cycles of adaptive alignment. To summarise, there are two main motivations for this paper. Firstly, utilizing innovation in adaptive methods to accelerate the convergence speed of initial alignment. Secondly, by using statistical methods to find deterministic short-term correction strategies, the accuracy and robustness of alignment can be improved under the premise of convergence. The effectiveness of the improved algorithms is verified by simulation and turntable experiments.