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为了提高微机械陀螺使用精度,研究了随机漂移误差补偿方法。对静态陀螺输出数据进行预处理,提取出随机漂移数据,采用时序分析方法对其建立AR模型。基于模型设计Kalman滤波器,进行静态和动态仿真。结果表明:静态条件下滤波效果显著;动态条件下,滤波效果变差。针对此问题设计了标量因子时变的自适应Kalman滤波器,试验表明,此算法能够有效降低微机械陀螺的随机漂移。
In order to improve the precision of micromechanical gyroscope, the random drift error compensation method is studied. The static gyro output data is preprocessed to extract the random drift data, and the time series analysis method is used to establish the AR model. Kalman filter based on model for static and dynamic simulation. The results show that the filtering effect is significant under static conditions and the filtering effect is worse under dynamic conditions. Aiming at this problem, an adaptive Kalman filter with time-varying scalar factors is designed. The experimental results show that this algorithm can effectively reduce the random drift of the micromechanical gyroscope.