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

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.