Abstract: The satellite clock bias is one of the important factors that affect the accuracy of navigation and positioning, so establishing a high-precision clock bias prediction model is of great significance to high-precision positioning. Aiming at the problem that satellite clock bias error accumulates by common models over time in short-term prediction, and the easy overfitting and instability of the traditional BP neural network, this paper proposed a model and algorithm of clock bias prediction based on BP neural network optimized by the mind evolutionary algorithm(MEA). First, original clock bias data made once difference to obtain the corresponding once difference sequences. Then, the initial weights and thresholds of the BP neural network were optimized by the mind evolutionary algorithm, the specific steps of using this model for the clock bias prediction were given. The multi-day GPS precision clock bias product data provided by the IGS station is used for experimental analysis. The article used the GPS data for the first 12 h of the day for modeling were listed, and made short-term clock bias prediction within 2, 3, 6 and 12 h. The results showed that the above four periods of prediction precision obtained by using the MEA-BP model were better than 0.36, 0.38, 0.62 and 1.56 ns, respectively. The fluctuation of the prediction error curve was small, and the prediction performance of the new model was better than the three traditional models, which showed the new model is better in practicability and stability in the short-term prediction of clock bias.

Key words: satellite clock bias, once difference, mind evolutionary algorithm(MEA), BP neural network, clock bias prediction