MEMS陀螺仪随机误差在线建模与滤波研究

Research on the online modeling and filtering of random errors of MEMS gyroscopes

针对普通微电子机械系统(micro-electro-mechanical system,MEMS)陀螺仪随机误差较大、零偏性影响在线建模的问题,提出对MEMS陀螺仪随机误差在线建模、实时滤波以提高MEMS陀螺仪在工程应用中的精度。首先,对陀螺仪采样数据进行分析处理,使其满足时间序列建模的要求。为避免数据离线零均值处理,实现在线建模,直接将数据中陀螺仪零偏引起的常值分量带入模型,建立具有常数项的时间序列模型,并通过递推最小二乘法得到模型参数的最优估计;然后,利用时域上状态空间方法设计增广状态Kalman滤波器,实时修正MEMS陀螺仪随机误差。研究表明方法有效,MEMS陀螺仪精度进一步提高,研究能为后续惯性测量运算提供可靠的数据支持。

Aiming at the problems of large random errors of MEMS(micro-electro-mechanical system,MEMS)gyroscopes and the influence of zero bias on online modeling,a method of online modeling and real-time filtering of random errors of MEMS gyroscopes was proposed to further improve the accuracy of MEMS gyroscopes in engineering applications.Firstly,the gyroscope sampled data was analyzed and processed to meet the requirements of time series modeling.In order to avoid the offline zero-average processing of the data and realize online modeling,the constant value components in the data due to the zero bias of the gyroscope was directly brought into the model.A time series model with constant terms was established,and the optimal estimates of model parameters were obtained by recursive least squares(RLS).Then,the augmented state Kalman filter was designed by using the state-space method in the time domain to correct the random errors of MEMS gyroscopes in real time.The results show that the method is effective,which further improves the precision of MEMS gyroscope and provides reliable data support for the subsequent inertial measurement operation.