为了提高微机电系统(MEMS)陀螺的测量精度, 提出了一种基于遗忘因子递推最小二乘估计的时变自回归滑动平均(ARMA)模型补偿方法.针对实测MEMS陀螺去除趋势项后的随机漂移信号, 采用分段检验方式进行了平稳性分析, 选取合适的基函数以及子空间维数进行时变ARMA模型建模.采用遗忘因子递推最小二乘估计的方式进行模型参数估计, 通过设置遗忘因子, 使得更新后的模型参数能够反映信号的动态变化.针对存在轻微波动的时变参数, 采用5阶多项式对时变模型参数进行拟合, 并提出一种解析法进行参数寻优, 从而建立最优随机漂移模型.将建模结果应用于卡尔曼滤波, 进行随机漂移补偿, 将补偿结果与时不变ARMA模型建模补偿方式的补偿结果进行对比发现, 所提方法补偿后的残差方差比时不变ARMA模型补偿后的残差方差降低了近40%, 有效提高了MEMS陀螺随机漂移的补偿精度.

In order to improve the measurement accuracy of Micro Electro Mechanical System (MEMS) gyroscope, a time varying Auto-Regressive and Moving Average (ARMA) Model compensation method based on forgetting factor recursive least squares estimation was proposed. According to the measured MEMS gyro random drift signal without trend item, the stability was analyzed by the subsection test and the time varying ARMA was built with the suitable basis function and subspace dimension. The model parameters were estimated with the Forgetting Factor Recursive Least Square (FFRLS) method by setting forgetting factor to make it possible that the model parameters can reflect the dynamic change of the signal. For the time varying parameters with slight fluctuation, the 5 order polynomial was used to fit the parameters of the time-varying model, and an analytical method was proposed to optimize the parameters, so as to establish the optimal random drift model. And the modeling results were applied to Kalman filter for random drift compensation. The compensation results of the proposed method were compared with the compensation results of the time invariant ARMA modeling compensation method. The comparison results indicated that the variance of the signal with the proposed method compensation is nearly 40% reduced by the variance of the signal with the time invariant ARMA model compensation. So the compensation precision of MEMS gyro random drift was improved effectively.