On Sigma-Point Update of Cubature Kalman Filter for GNSS/INS Under GNSS-Challenged Environment

The sampling-based Kalman filters have been widely used for GNSS/INS of land vehicle navigation. Although many nonlinear filters are proposed by employing different approximate algorithms, most of the filters update sigma points utilize the moments match and Bayesian filter update framework. In this paper, cubature Kalman filters (CKFs) based on a class of novel sigma-point update framework (NSUF) are developed and compared in terms of accuracy and convergence rate. In order to enhance the reliability of GNSS/INS, an improved CKF, named as ArtCKF is proposed by merging the moments generated by different NSUFs, which not only addresses the problem of observation missing but also improves the efficiency of Bayesian filter update. Numerical simulation based on field test data is employed to verify the superiority of ArtCKF in land vehicle navigation. Our results indicate that the ArtCKF improves the heading of land vehicle by 71.3% compared with CKF, and makes a tradeoff between direct observable and non-direct observable state estimation under GNSS-challenged environment.