基于ARMA-AKF的HRG随机误差建模分析

针对半球谐振陀螺(HRG)随机误差影响惯性测量单元测量精度的问题,提出了一种改进的基于自回归滑动平均(ARMA)模型和自适应滤波(AKF)的随机误差处理方法。该文对预处理的数据进行了自相关和偏相关特性分析,判断随机误差的适用模型,以及利用贝叶斯信息准则(BIC)准则估计ARMA模型的阶数,通过长自回归模型计算残差法获取模型参数,引入加权自适应因子在线调整一步预测误差阵和量测噪声矩阵用于改进滤波方程,并比较了5项主要误差系数值。结果表明,改进的算法能够有效抑制随机误差,为HRG的随机误差建模补偿提供了新方法。

Modeling and Analysis of HRG Random Error Based on ARMA-AKF Method

In view of the hemispherical resonator gyro(HRG) random error which affects measurement accuracy of inertial measurement unit, An improved processing method based on auto regressive and moving average (ARMA) model and adaptive Kalman filter(AKF) is proposed in this paper. First of all, the autocorrelation and partial correlation of the pretreated data is analyzed to determine the applicable model of the random error. Then the BIC rule is used to estimate the order of ARMA model, the model parameter is acquired through long autoregressive model residual error calculating method, the weighted adaptive factor is introduced to adjust online step prediction matrix and measure matrix which is used to improve filter equation. Finally, the five main error coefficients are compared. The result shows that the modified algorithm can effectively restrain the random error, which provides a new way for HRG random error modeling compensation.