Abstract:Aiming at the problem that satellite clock bias cannot be accurately modeled, this paper applies the Elman neural network with strong memory function and powerful computing ability to satellite clock bias prediction and proposes an Elman model suitable for satellite clock bias prediction. First, we perform one-difference process on the original clock bias data. Then, we select the appropriate neural network structure to establish the Elman clock bias prediction model with the best forecast effect. Finally, precise clock bias data provided by the international GNSS service(IGS) is used to predict GPS satellite clock bias, and comparison analysis is performed with the quadratic polynomial model, the polynomial model with additional periodic terms, and the gray system model. The experimental results show that the accuracy of 1-day, 7-days, and 30-days clock bias predictions of the Elman model have been significantly improved, reaching sub-nanosecond, nanosecond, and microsecond levels, respectively, proving that clock bias prediction performance of the model is superior to the three commonly used models and establishing the feasibility of the new model in satellite clock bias prediction.