关键词: 无迹卡尔曼滤波（UKF）, 奇异值分解（SVD）, 强跟踪, 渐消因子, 组合导航

Abstract: The performance of the Unscented Kalman filter would be degraded in accuracy or divergences when the system states are uncertain and strong nonlinear, an improved strong tracking SVD-UKF algorithm is proposed. The iteration of covariance matrix in UKF is improved by Singular Value Decomposition（SVD） of covariance matrix, ensured the stability of the iteration of covariance matrix and restrained the negative definiteness of system state covariance matrix. Multiple fading factors matrices are introduced in improved SVD-UKF, in order to automatic improve system state covariance matrix based on Strong Tracking Filter（STF） theory framework, and realize the strong tracking of the real state while system status are mutating. The proposed strong tracking SVD-UKF is applied to the BDS/INS integrated system for simulation, simulation results show the effectiveness of the presented algorithm.

Key words: Unscented Kalman Filter（UKF）, Singular Value Decomposition（SVD）, strong tracking, fading factor, integrated navigation