针对一般民用MEMS陀螺仪精度较低的缺点以及传统MEMS随机误差时间序列建模需进行零均值化处理且不能进行在线处理建模的问题,提出一种基于状态扩增的随机误差实时滤波方法, 将时间序列的均值作为未知数, 给ARMA模型增加一个截距项, 并利用该模型采用扩增状态的方法设计卡尔曼滤波及自适应卡尔曼滤波器, 使得测量数据不需满足零均值的条件。静态及摇摆试验表明, 所提方法能大幅提高MEMS陀螺仪精度。

A state amplification based method is proposed for real-time filtering of MEMS random error, which is used to overcome the disadvantage of civil MEMS gyroscopes of low precision, and to solve the problem of traditional MEMS that random error time series modeling needs zero-mean processing and cannot be modeled online.In the method, the mean of time series is considered as an unknown parameter, and an intercept term is added to the traditional ARMA model.A classical Kalman filter and an adaptive Kalman filter are designed by using the state amplification method based on this model, in which the filter has no zero-mean requirement for the measured data.Static and rocking experiments show that this method can significantly improve the accuracy of MEMS gyroscopes.