关键词: 奇异值分解, 简化的容积卡尔曼滤波, H&infin, 滤波, 自适应优化, GNSS/INS组合导航

Abstract: Under the condition of uncertainty of integrated navigation in system state model and noise statistical characteristics, the standard Cubature Kalman Filter（CKF） algorithm robustness is poor, which easily leds to the decrease of the filtering precision, even appears the problem of filtering divergence, a H∞ robust adaptive CKF algorithm is put forward. The algorithm is based on the standard theory framework of the third-order CKF algorithm, under the condition that the observation equation is linear, simplified update of its measurement, and introduces numerical stability strong Singular Value Decomposition（SVD）, which decomposes the state covariance matrix to improve the numerical stability of the calculation. In the update process of the system state covariance matrix, H∞ filtering method is introduced, and based on the theory of matrix inequality, make adaptive selection for its constraints [γ], further to improve the filtering stability and the robustness of the system. The algorithm is used in numerical simulation experiment of GNSS/INS integrated navigation, the results verify the validity and superiority of the algorithm.

Key words: Singular Value Decomposition（SVD）, simplified cubature Kalman filter, H&infin, filter, adaptive optimization, GNSS/INS integrated navigation