地球自转参数的RFFLS短期预报算法研究

发布时间：2018-04-02 00:31

本文选题：地球自转参数　切入点：预报　出处：《测绘通报》2017年09期

【摘要】：地球自转参数(ERP)是实现地心天球坐标系(geocentric celestial reference system,GCRS)与国际地球坐标系(international terrestrial reference system,ITRS)相互转换的必要参数,是国际GNSS服务组织(IGS)和国际GNSS监测评估系统(iGMAS)分析中心的重要产品。本文针对最小二乘地球自转参数预测算法会造成数据饱和以及新旧数据在数据处理及预报中被同等对待等问题,将遗忘因子引入最小二乘预测算法,进而提高ERP预报精度。遗忘因子递推最小二乘算法能防止数据饱和,降低旧数据的影响,加强新数据的作用,降低在求解拟合参数时出现秩亏矩阵求逆的几率,提高预报精度。本文详细推导了遗忘因子递推最小二乘表达式,探究了最佳遗忘因子,并通过ERP试验将该方法和原最小二乘的试验结果及LS-AR模型的预报结果作对比,发现仅用遗忘因子最小二乘模型预测就可以达到与LS-AR组合模型预测相当的精度。
[Abstract]:Geocentric celestial reference system is a necessary parameter to realize the interconversion between the geocentric celestial reference system and the international terrestrial reference system. It is also an important product of the International GNSS Service Organization (IGS) and the International GNSS Monitoring and Evaluation system (IGMAS) analysis center.Aiming at the problem that the least square earth rotation parameter prediction algorithm will cause data saturation and the old and new data are treated equally in the data processing and prediction, the forgetting factor is introduced into the least square prediction algorithm to improve the precision of ERP prediction.The recursive least square algorithm of forgetting factor can prevent the data saturation, reduce the influence of the old data, strengthen the function of the new data, reduce the probability of the rank deficiency matrix inverse in solving the fitting parameters, and improve the prediction accuracy.In this paper, the recursive least square expression of forgetting factor is derived in detail, and the optimal forgetting factor is explored. The results of this method are compared with those of the original least squares test and the prediction results of LS-AR model through ERP test.It is found that the prediction accuracy of the least square model with forgetting factor is comparable to that of the combined LS-AR model.
【作者单位】：山东科技大学;中国测绘科学研究院;
【基金】：国家自然科学基金(41474011) 国家重点研发计划(2016YFB0501405) 公益性行业专项(B1503) 国家基础测绘科技项目(2017KJ0205) 中国第二代卫星导航系统重大专项(GFZX0301040308-06)
【分类号】：P183.31;P228

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