低成本MEMS陀螺实时滤波方法

Real- time filtering method for low cost MEMS gyroscope

为找到一种普遍适合低成本MEMS陀螺仪的随机误差实时处理方法,利用Allan方差分析法首先对MEMS陀螺仪进行分析,根据其误差特性进而设计了平均滤波算法以剔除粗大误差,然后使用最小二乘法,通过拟合前一段历史结果得到下一时刻输出的预测值,基于以上工作最终设计出Kalman滤波器对所输出进行滤波。由于将最小二乘法的推测作为预测过程,避免了系统状态模型难以准确建立的问题。该方法动态性能好,具有普适性。实验结果证明,该方法在静态和动态下均能有效工作,滤波后常值漂移被有效补偿,角度随机游走不再占误差的主要成分,均方差小于滤波前的十分之一。

In this work, we aimed to find a general method suitable for coping in low cost Micro Electro Mechanical Systems （MEMS） gyroscope. First, Allan variance was utilized to analyze the drift error of MEMS gyroscope. According to its characteristic, we designed a real-time average estimate algorithm to eliminate gross error. Then, the least square algorithm was applied to extrap- olate the predicted value of next step through the previous output values. Based on the aforementioned works, we finally worked out a Kalman filter which efficiently reduced angle random walk and variance of output. This method can be applied to most of low cost MEMS gyroscope because the least square algorithm avoided the problem of being difficult to accurately model drift error. Test- ing results demonstrate that this method is available both in static and angular rate variation situations. After filtering, quite a bit of improvement is obtained： part of constant drift rate was compensated; raw measurement variance is reduced by more than 99 percent; random walk also has been effectively removed from random drift error.