基于Allan方差解耦自适应滤波的旋转SINS精对准方法

Refined alignment in rotary SINS based on Allan variance decoupling adaptive filter

对旋转式SINS精对准方法进行了研究,由于转位机构转动干扰以及惯性器件误差不确定性带来的影响,旋转式SINS状态方程和量测方程噪声方差参数难以确定,进而导致初始对准精度降低,针对这个问题引入自适应Kalman滤波技术。Sage-Husa是一种常用的自适应滤波算法,但是存在噪声参数强耦合缺陷。通过研究Allan方差与量测噪声方差之间的关系,利用Allan方差滤波器具有带通滤波的特点,独立计算量测噪声协方差阵R_k,该方法能够有效克服Sage-Husa滤波耦合问题,相比其它改进方法具有简单易实现等特点。对该研究进行了仿真实验与实际系统验证实验,结果表明:对于中等精度光纤陀螺单轴旋转SINS,自适应Kalman滤波算法航向角对准精度比标准Kalman滤波算法精度要高0.6’左右,且在误差估计过程中,自适应Kalman滤波器能够更好地抑制外界干扰误差的影响,是一种较好的精对准方法。

The refined alignment for rotary strapdown inertial navigation system（SINS） is studied. The adaptive Kalman filtering alignment is proposed to solve the problems of low filtering performance derived from uncertain noise caused by rotating disturbance of indexing mechanism and uncertain errors of inertial device in measurement equations. Sage-Husa is a generally used adaptive filtering algorithm, but it has the defection that the noise parameters are strongly coupled. Here, the relationship between the Allan variance and the measurement noise variance is studied by using the Allan variance filter which has the characteristics of band-pass filtering, and a filtering method is proposed, which can effectively overcome the strong coupling problem of traditional Sage-Husa filtering algorithm. The verifications by simulation and real inertial system are given, and the results show that the accuracy of the adaptive Kalman filtering algorithm is about 0.6’ higher than that of the conventional Kalman filtering algorithm, and the adaptive Kalman filtering algorithm is able to restrain the influence of the outside interference errors in the attitude error estimation process. Therefore, the adaptive Kalman filtering algorithm is a better refined alignment algorithm, which can be used to improve the accuracy of initial alignment of the rotary SINS.