基于GM(1,1)和D-MECM的钟差预报方法

针对单一钟差预报模型在建模数据量较少时中长期预报精度不足的问题,提出了基于灰色模型和一阶差分修正指数曲线法的组合预报模型。首先基于少量数据建立灰色模型并预测未来一段时间的钟差数据,再将其作为一阶差分修正指数曲线模型的建模数据,进行钟差的中长期预报。仿真结果表明,组合预报模型能够基于少量历史数据对钟差进行高精度的中长期预报。采用卫星共视仪采集的精密钟差数据进行实验,并与单一二次多项式模型和灰色模型进行对比,结果显示:使用5h的钟差数据进行建模并预报未来48h钟差数据时,二次多项式模型和灰色模型的平均预报精度分别为285.06ns和91.11ns,而组合模型的平均预报精度可达29.48ns,相比于单一二次多项式模型和灰色模型,分别提高了89.66%和67.64%。

Satellite Clock Bias Prediction Based on GM(1,1) and D-MECM

To improve the accuracy in medium-term and long-term prediction of a single satellite clock bias prediction model when the amount of modeling data is small, a combined prediction model based on grey model and first-order difference modified exponential curve method is proposed. In the model, a small amount of historical clock data is used to build a grey model and predict the clock data in the future, and then the prediction data is used as the modeling data of the first order difference modified exponential curve model to make the medium-term and long-term prediction of the clock. The simulation results show that the combined prediction model can forecast the clock difference with high accuracy based on a small amount of historical data. The experiment is carried out based on the precise clock difference data collected by the satellite common view instrument. Compared with the single quadratic polynomial model and the gray model, the results show that when using 5h clock difference data for modeling and forecasting 48h clock difference data in the future, the average prediction accuracy of quadratic polynomial model and gray model is 285.06ns and 91.11ns, respectively, while the average prediction accuracy of combined model is 29.48ns. Thus, compared with the single quadratic polynomial model and gray model, the proposed combined prediction model achieves 89.66% and 67.64% accuracies gains, respectively.