顾及卫星钟随机特性的抗差最小二乘配置钟差预报算法

王宇谱, 吕志平, 王宁, 李林阳, 宫晓春

doi:10.11947/j.AGCS.2016.20150569

测绘学报 >

2016 , Vol. 45 >Issue 6: 646 - 655

DOI: https://doi.org/10.11947/j.AGCS.2016.20150569

大地测量学与导航

顾及卫星钟随机特性的抗差最小二乘配置钟差预报算法

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1. 信息工程大学地理空间信息学院, 河南郑州 450001;
2. 地理信息工程国家重点实验室, 陕西西安 710054

王宇谱(1988-),男,博士生,研究方向为测量数据处理理论与方法。E-mail:987834660@qq.com

收稿日期: 2015-11-06

修回日期: 2016-01-12

网络出版日期: 2016-06-29

基金资助

国家自然科学基金(41274015;U1431115);国家863计划(2013AA122501);地理信息工程国家重点实验室开放研究基金(SKLGIE2015-M-1-6)

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Prediction of Navigation Satellite Clock Bias Considering Clock's Stochastic Variation Behavior with Robust Least Square Collocation

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1. School of Surveying and Mapping, Information Engineering University, Zhengzhou 450001, China;
2. State Key Laboratory of Geo-information Engineering, Xi'an 710054, China

Received date: 2015-11-06

Revised date: 2016-01-12

Online published: 2016-06-29

Supported by

The National Natural Science Foundation of China (Nos. 41274015;U1431115);The Natural High-tech Research and Development Program of China (863 Program) (No. 2013AA122501);The Open Research Fund of State Key Laboratory of Geo-information Engineering (No. SKLGIE2015-M-1-6)

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摘要

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。

关键词： 卫星钟差预报; 随机变化特性; 最小二乘配置; 抗差估计; 协方差函数

本文引用格式

王宇谱, 吕志平, 王宁, 李林阳, 宫晓春 . 顾及卫星钟随机特性的抗差最小二乘配置钟差预报算法[J]. 测绘学报, 2016 , 45(6) : 646 -655 . DOI: 10.11947/j.AGCS.2016.20150569

Abstract

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, and the corresponding prediction stability can improve 0.445 ns and 1.233 ns, compared with the results of quadratic polynomial model and grey model. In addition, the results also show that the proposed covariance function corresponding to the new model is reasonable.

Key words： satellite clock bias prediction; stochastic variation behavior; least square collocation; robust estimation; covariance function

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