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为了提高使用精度,研究了某微机电系统(MEMS,Micro Electro Mechanical System)陀螺仪的随机漂移模型.应用时间序列分析方法对经过预处理的陀螺仪量测数据进行建模,提出采用状态扩增法设计Kalman滤波器.进行速率试验和摇摆试验,验证了在静态和恒定角速率条件下,滤波后的误差均值和标准差分别为滤波前的55%和12%.针对在摇摆运动时随着振幅的增加滤波效果下降的问题,设计了自适应Kalman滤波器,分析了衰减因子的选取原则.仿真结果表明:常值衰减因子法和自适应衰减因子法都能显著改善摇摆运动时的滤波效果,而自适应衰减因子法的精度更高.
In order to improve the precision, a stochastic drift model of a MEMS (Micro Electro Mechanical System) gyroscope is studied.The time-series analysis method is used to model the preprocessed gyroscope measurement data, Kalman filter was designed.The rate test and the swing test were carried out to verify that the mean and standard deviation of the error after filtering were 55% and 12% respectively before and after filtering at static and constant angular velocities. The adaptive Kalman filter is designed and the selection principle of the attenuation factor is analyzed.The simulation results show that both the constant attenuation factor method and the adaptive attenuation factor method can significantly improve the filtering effect of the swinging motion , While the accuracy of the adaptive attenuation factor method is higher.