The Application of Functional Network in Medium- and Long-term Prediction of Clock Error of Navigation Satellite

Establishing an accurate clock error prediction model has a considerably practical significance for the navigation constellation.In order to reflect the real satellite clock error and predict the medium- and long-term clock error precisely,it is necessary to take the periodic and stochastic features of the clock error into consideration besides the clock's phase bias,frequency bias,and the linear rate of the frequency bias.Based on the traditional polynomial prediction model and functional network,a hybrid method is proposed in this paper.By using this method,the clock error series are fitted with the polynomial model firstly,and then the functional network is used to model the periodic and stochastic terms of clock error.We take the GPS satellites for example and compare five methods in predicting the clock error,which are the hybrid method proposed in this paper,the traditional polynomial model,the grey model,the autoregressive integrated moving average(ARIMA) model, and the Kalman filtering model.The results show that the hybrid method can reduce the prediction error effectively.