卡尔曼滤波在陀螺漂移时间序列模型中的应用

The Application of Kalman Filter in Modeling of Gyroscope Random Drift

针对MEMS陀螺精度不高、随机漂移过大,提出了一种提高精度的方法。利用时间序列分析法建立模型,用Matlab进行参数估计及检验,最后基于该模型设计了卡尔曼滤波器对其进行误差补偿。对某三轴MEMS陀螺的实测动态数据运用上述建立的模型和滤波方法进行仿真。结果表明:该方法有效的减小了随机误差,明显的降低了随机漂移,从而提高了导航精度。

In view of low precision and high random drift of MEMS gyroscope, an effective method for improving the accuracy of MEMS gyroscope was proposed. A model was established by time series analysis method and parameter was tested by Matlab, and a Kalman filter algorithm was designed for error compensation. A three-axis MEMS gyroscope＇ s dynamic data by measurement were simulated with the model and filtering method. The results show that the model and the filtering algorithm effectively reduce random error and significantly reduce random drift and improve navigation accuracy.