基于状态量扩维的旋转式捷联惯导系统精对准方法

初始对准是旋转式捷联惯导系统（SINS）的关键技术之一。传统旋转式捷联惯导精对准方法多采用10维模型，该模型的精对准精度不能满足导航精度要求。针对此问题，提出了一种基于状态量扩维的旋转式捷联惯导系统精对准方法。首先，将陀螺和加速度计标度因数误差、安装误差扩展为状态变量，建立了28维的精对准模型；然后，对旋转过程中各状态量的可观测度进行分析，根据分析结果将模型优化为13维；最后，采用卡尔曼滤波实现了旋转式捷联惯导系统的精对准。仿真结果表明，与传统初始对准方法相比，该方法能有效提高姿态对准精度，并估计出更多陀螺误差项。 

Fine alignment method for rotary strapdown inertial navigation system based on augmented state

Initial alignment is a key technology of rotary strapdown inertial navigation system (SINS). The existing 10D model of traditional rotary SINS is mostly used for the fine alignment, which cannot meet the requirements of navigation accuracy. To solve this problem, a fine alignment method for rotary SINS based on augmented state is proposed. First, scale factor errors and installation errors are extended to state variables, and a 28D fine alignment model is established. Second, the observability of 28D model during rotation is analyzed. Third, a 13D alignment model is designed according to analysis results. Finally, Kalman filter is used to achieve the fine alignment of rotary SINS. The simulation results show that the proposed method can effectively improve the attitude alignment accuracy and estimate more gyroscope error terms compared with the traditional initial alignment method.