Application of Improved CKF in SINS Initial Alignment with Large Misalignment Angles

Abstract

In order to improve the alignment accuracy and reduce time for the initial alignment of SINS, an improved CKF method is proposed. SINS nonlinear error model with large initial misalignment angles is built up. Based on the basic algorithm of CKF, multiple fading factors are introduced to the covariance matrix of the prediction errors to modulate gain matrix online in real-time for each data channel, which can improve the accuracy and robustness of the algorithm; Singular Value Decomposition is used instead of the traditional Cholesky decomposition of CKF to improve the stability of the algorithm. Experiment results show that the alignment time for azimuth angle of improved CKF is 100 seconds shorter than CKF, the alignment accuracy improved by 40% compared with CKF, and the alignment accuracy of azimuth angle is less than 0.1°. The experimental results show that the improved CKF effectively improves the alignment accuracy under the premise of higher speed, which better fits SINS initial alignment for large misalignment angles.