关键词: 容积卡尔曼滤波, 混合阶, 球面最简相径, 自适应, 多重渐消因子, 组合导航

Abstract: The traditional Cubature Kalman Filter（CKF） has problems of low accuracy and even divergence when dealing with highly nonlinear systems, and High-Degree Cubature Kalman Filter（HCKF） increases the accuracy while greatly increasing the computational complexity, at the same time, negative weight affects the stability of the algorithm in high-dimensional systems. In view of the above problems, this paper proposes an Adaptive Mixed-degree Spherical Simplex-Radial Cubature Kalman Filter（AMSSRCKF）, which adopts Mixed-degree Spherical Simplex-Radial（MSSR） sampling to obtain higher accuracy than CKF, and combines with the strong tracking filter algorithm of multiple fading factors to improve the robustness of the algorithm. The algorithm is applied to the simulation of the integrated navigation system, the results show that AMSSRCKF can effectively suppress the impact of the sudden change of the system state and improve the positioning accuracy and robustness of the integrated navigation system.

Key words: Cubature Kalman Filter（CKF）, mix-degree, spherical simplex-radial, adaptive, suboptimal multiple fading factor, integrated navigation