Abstract: In order to improve the alignment time, initial alignment is carried on with large azimuth misalignment, and the nonlinear filtering methods are utilized. Therefore Gaussian Process regression Square Root Central Difference Kalman Filtering(GP-SRCDKF) is proposed, and which is taken Gaussian process regression into SRCDKF algorithm to get system regression model and noise covariance, regression model is taken instead of state equation and observation equation, and the corresponding noise covariance makes real-time adaptive adjustment, which not only overcomes the deficiencies that Extended Kalman Filtering(EKF) has low precision and needs to calculate the Jacobian matrix, but also solves the problems that traditional filter is limited by the uncertain system dynamic model and inaccurate noise covariance. Simulation results verify the effectiveness and superiority of the proposed algorithm.
large azimuth misalignment,
strapdown inertial navigation,
Gaussian Process regression Square Root Central Difference Kalman iltering(GP-SRCDKF),