Sage_Husa自适应滤波在大方位失准角初始对准的研究

由于光纤惯导系统导航精度不高，方位角常为大角度，因此系统初始对准的滤波方程为非线性的，为改善非线性模型下初始对准的精度，提出了一种改进Sage_Husa自适应卡尔曼滤波方法并应用于光纤惯导系统初始对准中。建立了大方位失准角初始对准的非线性误差模型，给出了Sage_Husa自适应卡尔曼滤波方程，对Sage_Husa自适应卡尔曼滤波不适合用在非线性滤波的缺陷进行了改进，建立系统噪声统计的估值器，对非线性误差方程进行了改进Sage_Husa自适应卡尔曼滤波仿真。仿真结果表明:改进Sage_Husa自适应卡尔曼滤波能够很好地处理初始对准中的非线性问题，提高初始对准精度，方位失准角误差估计精度较EKF提高27%。

Research on initial alignment for large azimuth misalignment angle with Sage_Husa adaptive filtering

The azimuth was often a large angle because navigation accuracy of fiber-optic inertial navigation system was not high, and the filtering equations of initial alignment were non-linear. In order to improve the initial alignment accuracy of nonlinear models, a improved Sage_Husa adaptive kalman filtering method was put forward, and applied to initial alignment of fiber-optic inertial navigation system. Established initial alignment nonlinear model of large azimuth misalignment angle, contributed the system noise statistics estimators, and used improved Sage_Husa adaptive kalman filtering to simulate for nonlinear error equations. The simulation results show that the improved Sage_Husa adaptive kalman filtering could deal with nonlinear problems, improve the accuracy of initial alignment. Azimuth misalignment angle error estimation precision improved 27% than EKF.