【摘要】 为了提高某型微机电系统(MEMS)陀螺仪输出精度,静态采集该型MEMS陀螺仪原始数据,通过Allan方差分析法,对陀螺仪随机误差成分进行辨识;以z轴输出为例,利用时间序列分析法,建立其随机误差的自回归滑动平均(ARMA)模型。根据拟合后的模型参数设计卡尔曼滤波器,对原始数据进行滤波处理,再对预滤波后的数据进行Allan方差分析。结果表明:滤波后的量化噪声、角度随机游走、零偏不稳定性误差系数分别减小了2. 8%,19. 8%和8. 1%。卡尔曼滤波器能够有效抑制MEMS陀螺仪的随机误差,提高输出精度。更多还原

【Abstract】 To improve the output precision of a certain type of micro-electro-mechanical system( MEMS)gyroscope,the raw data of the MEMS gyroscope are acquired in static state. The Allan variance analysis method is applied to identify the components of random errors of the gyroscope. An auto-regressive and moving average( ARMA) model of z axis output is built by time series analysis method. Kalman filter is designed based on fitted model parameters to process the raw data,after which,the Allan variance analysis is performed on the pre-filtered data. The result shows that the error coefficients of quantization noise( QN),angular random walk( ARW) and bias instability( BI) decrease by 2. 8 %,19. 8 % and 8. 1 % respectively,Kalman filter can restrain random errors of MEMS gyroscope and improve its precision effectively.更多还原