基于中心差分卡尔曼滤波的SINS初始对准研究

Study of SINS Alignment Based On Central Difference Kalman Filter

研究了在大失准角条件下,捷联惯导系统初始对准非线性误差模型,分析了扩展卡尔曼滤波算法因对非线性模型线性化而存在的缺点,研究了中心差分卡尔曼滤波算法,提出将中心差分卡尔曼滤波算法应用于捷联惯导系统大失准角初始对准中。仿真结果表明,在水平失准角为小角度、方位失准角为大角度时,中心差分卡尔曼滤波算法同扩展卡尔曼滤波算法相比,提高了初始对准的估计精度和收敛速度。

The nonlinear error equation of Strapdown Inertial Navigation System（SINS） under large heading uncertainty for SINS alignment was built up. Extended Kalman Fiter（EKF）＇s drawbacks aroused by the linearization of nonlinear model were analyzed, Central Difference Kalman Filter（CDKF） was studied and applied to INS initial alignment under large heading uncertainty. Simulation results show that CDKF is faster in convergence and more accurate in estimate precision than EKF in the case of low uncertainties in tilt angle and large heading uncertainty.