中文摘要：

卫星导航系统中星载原子钟的钟差预报在优化导航电文中的钟差参数、满足实时动态精密单点定位的需求和提供卫星自主导航所需的先验信息方面具有重要的作用。根据星载原子钟钟差的特点,提出一种基于一次差方法的小波神经网络钟差预报算法：首先对钟差相邻历元间作一次差后的差值进行建模,根据时间序列预报一次差的值,然后再将预报的一次差还原,得到钟差预报值。该方法使得预报钟差的小波神经网络不但模型结构简单,而且预报精度高。最后,通过算例将本文所建模型与常用的二次多项式模型和灰色模型进行对比,结果表明：一次差方法可以使给定结构的小波神经网络的钟差预报精度得到显著提高,而且所建模型的预报效果优于两种常规模型。

英文摘要：

Satellite clock bias （SCB） prediction plays an important role in satellite navigation system, such as optimizing clock parameters in navigation message, meeting the needs of real-time dynamic precise point positioning and providing the required information for satellite autonomous navigation. It is proposed that awavelet neural network （WNN） model based on a once difference method to predict clock bias considering the characteristic of SCBin this paper. The main ideas are as follows： two SCB values of adjacent epoch firstly make once difference to obtain the corresponding once difference sequences, then modeling based on the sequences to predict once difference values of the following time series. At last, the predicted sequences are recovered to the corresponding predictedSCB. This method makes that theWNN model of SCB prediction is simple in structure and the predicting precision is higher. Finally through predicting examples, the new model is compared with two frequently-used models, for example, quadratic polynomial （QP） model and GM（1,1） model. The results show that the once difference method can evidently improve the prediction precision of SCB for the given WNN, and the results also turn out that the new model possesses better performance than the common models in SCB prediction.