基于逆向导航算法的捷联式惯性导航系统改进优化对准方法

快速性和精度是捷联式惯性导航系统动基座初始对准的重要指标。优化对准方法（OAM）在短时间内难以获取足够多的观测信息，导致对准性能降低。针对此问题，提出一种基于逆向导航算法的改进动基座初始粗对准（IMCA）方法。通过逆向导航算法对存储的陀螺仪和加速度计数据进行虚拟延长并加以反复利用，扩展积分区间长度，以构建新的观测矢量、实现对准精度的提升。推导了载体坐标系下的OAM，分析了观测矢量包含的信息量对姿态确定精度的影响。基于车载实测数据，分别利用OAM和IMCA方法进行动基座初始对准试验，结果表明：相比于OAM，IMCA方法可在相同条件下实现更高精度的初始对准；IMCA方法应用在载体坐标系下动基座初始对准中是可行、有效的。

An Improved Optimal Alignment Method of Strapdown Inertial Navigation System Based on Reverse Navigation Algorithm

Rapidity and precision are the important indicators for in-motion initial alignment of strapdown inertial navigation system (SINS). The optimized alignment method (OAM) can not be used to get enough observation information in a short time, which may degrade the alignment performance. An improved in-motion coarse alignment (IMCA) method based on the reverse navigation algorithm is proposed. In the IMCA method, the stored gyroscope and accelerometer data are reused and the integration procedure is extended using the reverse navigation algorithm. The extended integration procedure is used to construct new observation vector to achieve the improvement of alignment accuracy. The OAM in the body frame (b-frame) is derived, and then the influence of quantity of observation information on the precision of attitude determination is analyzed. The in-motion alignment experiments were carried out by using OAM and IMCA method based on the measured data of vehicle test, respectively. The experimental results demonstrate that the IMCA method can achieve higher alignment accuracy at the same conditions compered with OAM. The IMCA method can be effectively applied to the b-frame in-motion alignment.