基于Allan方差解耦自适应滤波的旋转SINS精对准方法

发布时间：2018-12-05 20:05

【摘要】：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。
[Abstract]:The precision alignment method of rotary SINS is studied. The noise variance parameters of rotating SINS state equation and measurement equation are difficult to determine due to the influence of rotating interference of the translocation mechanism and the uncertainty of inertial device error. In order to solve this problem, adaptive Kalman filtering technique is introduced. Sage-Husa is a commonly used adaptive filtering algorithm, but it has strong coupling defects of noise parameters. By studying the relationship between Allan variance and measurement noise variance, using the characteristic of bandpass filter, Allan variance filter can independently calculate the measurement noise covariance matrix R _ s _ k. This method can effectively overcome the coupling problem of Sage-Husa filter. Compared with other improved methods, the method is simple and easy to realize. The simulation experiment and the actual system verification experiment are carried out. The results show that the heading angle alignment accuracy of the SINS, adaptive Kalman filtering algorithm is about 0.6 'higher than that of the standard Kalman filtering algorithm. In the process of error estimation, adaptive Kalman filter can better restrain the influence of external interference error, so it is a better precision alignment method.
【作者单位】：东南大学微惯性仪表与先进导航技术教育部重点实验室;
【基金】：国家自然科学基金项目(61374215)
【分类号】：TN96

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