Iterative algorithms based on saved data have been researched for strapdown inertial navigation system to make the best use of sensor data, and different alignment algorithms can be carried out during the iterative process to fulfil initial alignment. This paper firstly analysed two interactive processes. The forward-backward process needs to change the normal system model in the backward process and have additional error factors. While the forward-forward process is easy to be carried out without changing the system model, the transition error will also decrease the accuracy. In addition, traditional iterative methods always need to save a large amount of data and do redundant computation, which is not highly effective, especially in view of the real-time property. To address these effects, a forward-forward solution in the inertial frame based on Kalman filter is analysed. This method calculates the attitude transfer matrix in the inertial frame to deal with angular disturbances and avoids saving all the original measurement data during the process, which makes the method more stable and faster to fulfil in the real-time system. Simulation and turntable test validates the performance of the proposed method.

中文翻译：

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惯性框架中基于迭代算法的快速捷联惯导初始对准方法

对于捷联惯性导航系统，已经研究了基于保存数据的迭代算法，以充分利用传感器数据，并且在迭代过程中可以执行不同的对准算法来实现初始对准。本文首先分析了两个交互过程。前向-后退过程需要在后向过程中更改常规系统模型，并具有其他误差因素。尽管在不更改系统模型的情况下很容易执行前向过程，但转换误差也会降低精度。另外，传统的迭代方法总是需要保存大量数据并进行冗余计算，这并不是很有效，特别是考虑到实时性。为了解决这些影响，分析了基于卡尔曼滤波的惯性系中的正解。该方法通过计算惯性系中的姿态传递矩阵来处理角度干扰，避免了在处理过程中保存所有原始测量数据，使得该方法在实时系统中更稳定，更快速地实现。仿真和转台测试验证了所提方法的性能。