Abstract:Inertial navigation system needs initial alignment before normal operation, cubature Kalman filter (CKF) is a common algorithm for nonlinear initial alignment. Aiming at the problems that accuracy decline or even divergence appear in conventional cubature Kalman filter under the conditions of inaccurate filtering model and nonGaussian observation noise interference, a robust fading CKF algorithm is proposed in this paper. Multiple fading factors are introduced to adjust the observation noise covariance matrix or state prediction covariance matrix. A filter state Chisquare test method based on the statistical characteristics of filtering residual sequence is designed to check the filter state, and determine the introducing means of the fading factors autonomously, which makes the introduction of the fading factors is more reasonable. Experiment results show that the proposed algorithm can maintain strong robustness and adaptability even under the conditions of inaccurate system modeling and abnormal nonGaussian observation noise interference. The attitude misalignment error is about 001 ° and the yaw misalignment error is less than 01°.