顾及卫星钟随机特性的抗差最小二乘配置钟差预报算法

为了更好地反映钟差特性并提高其预报精度,采用抗差最小二乘配置方法建立一种能够同时考虑星载原子钟物理特性、钟差周期性变化与随机性变化特点的钟差预报模型。首先使用附有周期项的二次多项式模型进行拟合提取卫星钟差的趋势项与周期项,然后针对剩余的随机项及其可能存在的粗差,采用抗差最小二乘配置的原理进行建模,其中最小二乘配置的协方差函数通过对比协方差拟合的方法并结合试验进行确定。使用IGS精密钟差数据进行预报试验,将本文方法与二次多项式模型、灰色模型进行对比,预报精度分别提高了0.457 ns和0.948 ns,而预报稳定性则分别提高了0.445 ns和1.233 ns,证明了本文方法能够更好地预报卫星钟...

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Bibliographic Details
Published in	测绘学报 Vol. 45; no. 6; pp. 646 - 655
Main Author	王宇谱吕志平王宁李林阳宫晓春
Format	Journal Article
Language	Chinese
Published	地理信息工程国家重点实验室,陕西西安710054%信息工程大学地理空间信息学院,河南郑州,450001 2016 信息工程大学地理空间信息学院,河南郑州450001
Subjects	协方差函数卫星钟差预报抗差估计最小二乘配置随机变化特性卫星钟差预报抗差估计最小二乘配置 least square collocation robust estimation 协方差函数随机变化特性 satellite clock bias prediction stochastic variation behavior covariance function
Online Access	Get full text
ISSN	1001-1595
DOI	10.11947/j.AGCS.2016.20150569

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Summary:	为了更好地反映钟差特性并提高其预报精度,采用抗差最小二乘配置方法建立一种能够同时考虑星载原子钟物理特性、钟差周期性变化与随机性变化特点的钟差预报模型。首先使用附有周期项的二次多项式模型进行拟合提取卫星钟差的趋势项与周期项,然后针对剩余的随机项及其可能存在的粗差,采用抗差最小二乘配置的原理进行建模,其中最小二乘配置的协方差函数通过对比协方差拟合的方法并结合试验进行确定。使用IGS精密钟差数据进行预报试验,将本文方法与二次多项式模型、灰色模型进行对比,预报精度分别提高了0.457 ns和0.948 ns,而预报稳定性则分别提高了0.445 ns和1.233 ns,证明了本文方法能够更好地预报卫星钟差,同时说明本文的协方差函数确定方法的有效性。
Bibliography:	11-2089/P In order to better express the characteristic of satellite clock bias （SCB） and further improve its prediction precision, a new SCB prediction model is proposed, which can take the physical feature, cyclic variation and stochastic variation behaviors of the space-borne atomic clock into consideration by using a robust least square collocation （LSC） method, The proposed model firstly uses a quadratic polynomial model with periodic terms to fit and abstract the trend term and cyclic terms of SCB. Then for the residual stochastic variation part and possible gross errors hidden in SCB data, the model employs a robust LSC method to process them. The covariance function of the LSC is determined by selecting an empirical function and combining SCB prediction tests. Using the final precise IGS SCB products to conduct prediction tests, the results show that the proposed model can get better prediction performance. Specifically, the results＊ prediction accuracy can enhance 0. 457 ns and 0. 948 ns respectively,
ISSN:	1001-1595
DOI:	10.11947/j.AGCS.2016.20150569