关键词: 后向平滑, 组合导航, 平方根容积卡尔曼滤波, 自适应滤波

Abstract: [Purposes] Aiming at filtering diverges caused by not-positive definite state covariance matrix of cubature Kalman filter (CKF)in practical applications and the system is unstable when there are noise anomalies,in this paper,an improved backward smoothing robust adaptive SRCKF integrated navigation algorithm is proposed. [Methods] On the basis of the SRCKF algorithm,backward smoothing is employed to improve the accuracy of filtering.Additionally,an adaptive factor has been introduced to adjust the cross-covariance matrix between the predicted state vector and the measurement vector,further mitigating the impact of observation anomalies on the system. [Findings] Through the analysis of simulation and measured data experiments,the proposed algorithm has higher filtering accuracy,and has a good effect on reducing the impact of measurement noise mutation. [Conclusions] It improves the anti-interference ability of the system,and provides reference value for the post-processing of GNSS/INS integrated navigation data.

Key words: backward smoothing, integrated navigation, square-root cubature Kalman filter, adaptive filtering