中文摘要：

为了提高导航卫星钟差中长期预报的精度,在提出一种针对钟差一次差分序列的数据预处理方法的基础上,建立了一种钟差中长期预报的小波神经网络模型。该模型首先对建模钟差数据进行一次差分,然后对一次差分序列进行预处理;用预处理后的一次差分序列对小波神经网络建模并进行中长期预报,最后将预报结果还原得到相应的钟差预报值。使用全球定位系统（GPS）卫星的铷钟数据进行中长期预报,并与常用的二次多项式模型、灰色模型、Kalman滤波模型进行对比,结果表明,本文方法能有效减小导航卫星星载铷钟钟差的中长期预报误差。

英文摘要：

In order to improve the prediction precision of navigation satellite clock bias in the medium and long term, we design a new prediction model using a wavelet neural network based on a new data preprocessing method, aimed at processing the single difference sequence of satellite clock bias data. Specifically, this model firstly makes difference between two values of adjacent epoch for the given clock bias data, thus obtaining the corresponding single difference sequence, and then uses the pro- posed preprocessing method to process the sequence, and adopts the preprocessed sequence when modeling a wavelet neural network to predict the following medium- and long-term sequences. Final- ly, the proposed model restores the predicted sequences to the corresponding prediction clock bias. U- sing clock bias data from satellite-bone rubidium clocks in GPS, we conducted medium- and long-term prediction tests for the new method, simultaneously comparing it with three common prediction meth- ods ~the quadratic polynomial model, grey model, and Kalman filter model. The results show that the new method can effectively reduce the prediction error in the medium- and long-term satellite clock bi- as prediction.