Research on short-term clock bias prediction of BeiDou satellite based on optimized residual difference combination

In order to solve the problem of poor prediction accuracy caused by the traditional model using the satellite clock bias primary difference sequence and further improve the prediction accuracy, an optimized residual combination is proposed to forecast the satellite clock bias primary difference sequence. This method firstly according to the characteristics of the beidou satellite clock bias sequence, using quarterback method instead of the median method of time difference sequence preprocessing, and then using autoregressive moving average (ARMA) model after preprocessing the satellite clock bias of a differential sequence is divided into trend item and random item residual, then using the extreme learning machine (ELM) model to simulate the residual part modeling prediction, Finally, the prediction results of ARMA model and residual prediction results of ELM neural network are summed and then differentially restored. The results show that when the satellite clock bias is nonlinear, the prediction accuracy of the combined model is 38.2% higher than that of the traditional model, which has certain feasibility in the short-term prediction of the BeiDou satellite clock bias.