基于灰色模型和混沌时间序列的卫星钟差预测算法

为了提高非线性卫星钟差预测的精度，降低单一钟差预测模型对钟差预测的风险，提出了一种组合模型的卫星钟差预测算法.该算法首先采用db1小波对卫星钟差序列进行3层多分辨率分解和单支重构，得到一个趋势分量和三个细节分量，然后运用灰色预测模型对重构后的趋势分量和混沌一阶加权局域预测法对重构后的细节分量分别进行预测，最后将各分量预测结果相加后得到总的钟差预测值.以GPS卫星钟差数据做算例分析，在6小时的钟差预测中，算法绝对误差最大值比单一的灰色预测模型误差小1.3ns以上.将该组合预测模型用于非线性卫星钟差预测中，可以提高钟差预测的精度和可靠性.

A Satellite Clock Bias Prediction Algorithm Based on Grey Model and Chaotic Time Series

In order to improve the accuracy of nonlinear satellite clock bias prediction and reduce the risk of single clock bias prediction model for clock bias prediction,a satellite clock bias prediction algorithm based on combined model is proposed.The algorithm firstly uses db1 wavelet to conduct a 3 layer multiresolution decomposition and single branch reconstruction of satellite clock bias sequences,and obtains a trend component and three detail components.Then the grey prediction model is used to predict the reconstructed trend component and the chaotic time series one-order weighted local prediction method is used to predict the reconstructed detail components.Finally,each component prediction result is added to obtain the total clock bias prediction value.Taking the GPS satellite clock bias data as an example,the maximum absolute error of the algorithm is at least 1.3ns smaller than that of a single gray prediction model in 6-hour clock bias prediction.This combined prediction model can be applied to the prediction of nonlinear satellite clock bias,which can improve the accuracy and reliability of clock bias prediction.