摘要
针对卫星钟差呈趋势项和随机项变化的特点,提出了基于GM(1,1)与自回归求和移动平均的组合预报模型。该模型首先采用GM(1,1)模型预报钟差的趋势项部分,然后利用ARIMA模型对GM(1,1)的模型残差序列进行建模和预报,最后将GM(1,1)和ARIMA模型的预报结果对应相加即得到钟差的最终预报值。此外,采用IGS公布的精密卫星钟差进行预报试验,通过与卫星钟差预报中常用的二次多项式模型和修正指数曲线法模型预报结果的对比分析,结果表明:该方法可以对GPS卫星钟差进行高精度的中短期预报。用12 h钟差建模时,预报未来6、12、24和48 h的平均预报精度分别为0.71、1.17、1.93和4.38 ns,相比于二次多项式模型的平均预报精度分别提高了29.70%、43.75%、67.62%和76.21%;相比于修正指数曲线法模型的平均预报精度分别提高了18.39%、33.90%、61.40%和70.49%。
Based on the characteristics of the trend and random items of the satellite clock bias （SCB）, a combination prediction model based on grey model（ GM（ 1,1 ） ） and Autoregressive Integrated Moving Average（ARIMA） is proposed.First,the model uses GM （ 1, 1 ） to predict the trend of SCB.Then,the residual sequence of GM（1,1） model is modeled and predicted by using the ARIMA.Finally, the prediction results of GM （1,1） and ARIMA are added to obtain the final prediction value of clock bias.In addition, the predictive tests are carried out by using the precision SCB published by IGS（ International GNSS Service） , and the results are compared with those of the quadratic polynomial model（QPM） commonly used in SCB forecast and using the modified exponential curve method （MECM）. The results show that this method can make high-precision short-term and mid-term forecast of GPS SCB. When modeling with 12 h clock bias data to predict the next 6,12,18 and 24 h,the average prediction accuracy of the model we proposed is 0.71,1.17,1.93 and 4.38 ns,respectively.Compared with the mean prediction accuracy of QPM, the accuracy of the prediction was increased by 29.70%, 43.75%,67.62% and 76.21%,respectively.Compared with the mean prediction accuracy of MECM,the accuracy of the prediction was increased by 18.39% ,33.90% ,61.40% and 70.49% ,respectively.
作者
于烨
张慧君
李孝辉
YU Ye;ZHANG Huijun;LI Xiaohui(National Time Service Center, Chinese Academy of Science, Xi＇ an 710600, China;Key Laboratory of Precision Navigation and Timing Technology, National Time Serviee Center, Chinese Academy of Science, Xi＇ an 710600, China;University of the Chinese Academy of Science Beijing 100049, China;School of Astronomy and Space Science, University of the Chinese Academy of Science, Beijing 100049, China)
出处
《测绘通报》
CSCD
北大核心
2018年第6期1-6,共6页
Bulletin of Surveying and Mapping
基金
国家自然科学基金(11503030)
国防创新基金面上项目(CXJJ-16M205)
中国科学院"西部之光"人才培养计划支持项目(Y507YR0501)
