小型动调陀螺随机误差建模与滤波方法研究

Research on random error modeling and filtering method for dynamically tuned gyroscope

为了抑制动力调谐陀螺的随机漂移,采取时间序列分析的方法,分析了φ35小型动力调谐陀螺仪输出数据的平稳性,建立了其随机漂移的自回归求和滑动平均(autoregressive integrated moving average,ARIMA)模型。以所建模型作为状态方程、实际测量数据作为量测值,设计了卡尔曼滤波器,并应用卡尔曼滤波器对实际测量的动力调谐陀螺输出数据进行了滤波,处理后陀螺随机漂移仅为原数据的46.7%。结果表明,滤波方法能有效地抑制陀螺的随机漂移,同时也验证了所建模型的正确性和有效性。此方法也可应用于其他类型陀螺的输出数据处理。

In order to restrain the random drift of φ35 dynamically tuned gyroscope （DTG） and improve its accuracy, the time series analysis method is used to analyze its output data stationarity and establish the non-stationary time series autoregressive integrated moving average （ARIMA） model of the DTG random drift. Taking the model as state equation and the gyroscope real output data as measurement values, a Kalman filter is designed and used to filter the DTG real output data. After data processing, the DTG random drift decreases to 46.7% of its original value. The filtering results prove the correctness and validity of the established model, and indicate that the filtering method can effectively restrain the random drift of the gyroscope. This method can also be used to process the output data of other kinds of gyroscopes.