一种MEMS陀螺随机漂移的高精度建模方法

为补偿MEMS陀螺随机漂移，采用时间序列分析法对其进行自回归滑动平均（ARMA）模型辨识，提出一种滑动平均（MA）参数估计的新方法。先将陀螺随机漂移建模为带观测噪声的ARMA模型，在估计出自回归（AR）部分的参数后，针对AR滤波后的残差，推导出一种方差小的MA自协方差估计值，并将该估计值作为输入，利用Gevers-Wouters（GW）算法估计出MA部分的参数。仿真结果表明，MA参数估计精度得到提升的同时，参数估计可靠性也得到了增强。MEMS陀螺的随机漂移补偿实验进一步验证本文所提算法的补偿精度高于改进前。

An accurate modeling method for random drift of MEMS gyro

To compensate random drift in MEMS gyros, an auto-regressive moving average (ARMA) model for measured data drift was developed using time series analysis, and a new estimation method was proposed for moving-average (MA) models. The gyro noise was modeled as an ARMA with the observation noise. After the auto-regressive (AR) parameters were estimated, a more accurate estimation with a smaller variance of the MA autocovariance sequence was deduced for the residual noise by the AR filtering. The statistics were used as the input of Gevers-Wouters (GW) method to estimate MA parameters. The results of simulation prove that both the accuracy and reliability of parameter estimation are improved. The compensation experiment of MEMS gyros random drift further verifies that the proposed method is more accurate than the traditional one.