利用Allan方差分析GPS非差随机模型特性

张小红; 朱锋; 薛学铭; 唐龙

doi:10.11947/j.AGCS.2015.20130513

测绘学报 >

2015 , Vol. 44 >Issue 2: 119 - 127

DOI: https://doi.org/10.11947/j.AGCS.2015.20130513

大地测量学与导航

利用Allan方差分析GPS非差随机模型特性

张小红 ,
朱锋 ,
薛学铭 ,
唐龙

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武汉大学测绘学院, 湖北武汉 430079

张小红(1975—),男,教授,博士生导师,主要从事GNSS技术及其应用。E-mail:xhzhang@sgg.whu.edu.cn

收稿日期: 2013-12-09

修回日期: 2014-10-28

网络出版日期: 2015-02-14

基金资助

国家自然科学基金(41474025); 高等学校博士学科点专项科研基金(20130141110001)

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Using Allan Variance to Analyze the Zero-differenced Stochastic Model Characteristics of GPS

ZHANG Xiaohong ,
ZHU Feng ,
XUE Xueming ,
TANG Long

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School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China

Received date: 2013-12-09

Revised date: 2014-10-28

Online published: 2015-02-14

Supported by

The National Natural Science Foundation of China(No.41474025);Specialized Research Fund for the Doctoral Program of Higher Education of China(No.2013141110001)

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摘要

非差GPS定位中, 通常采用以观测值服从高斯白噪声条件的估计准则进行参数求解。研究表明卫星端误差、传播路径误差、测站环境误差等会破坏观测值的白噪声特性, 并且未模型化误差同样具有不利影响。这不仅破坏了估计准则的假设条件, 而且部分非白噪声有可能被状态参数吸收, 影响估计的准确性。本文将观测值白噪声、有色噪声和未模型化误差一同纳入GPS非差随机模型, 以验后残差来表征GPS数据的随机特性进行Allan方差分析, 研究噪声成分及其参数。结果表明, GPS非差噪声组合主要为WN+GM, 相位白噪声为2.392 mm, GM过程噪声为4.450 mm/√s, 相关时间为52.074 s, 伪距白噪声为0.936 m, GM过程噪声为0.833 m/√s, 相关时间为14.737 s, 相位的GM过程噪声与卫星相关性较大, 而其余噪声则与测站相关性较大, 大量分析结果表明GPS非差随机模型并不服从高斯白噪声假设, 有待精化。

关键词： Allan方差; GPS数据; 随机模型; 噪声成分; 噪声参数; 验后残差

本文引用格式

张小红 , 朱锋 , 薛学铭 , 唐龙 . 利用Allan方差分析GPS非差随机模型特性[J]. 测绘学报, 2015 , 44(2) : 119 -127 . DOI: 10.11947/j.AGCS.2015.20130513

Abstract

The estimation criteria for solving parameters in zero-differenced GPS positioning is that observations obey Gaussian white noise distribution. But a number of pioneering studies point out that the white noise would be damaged by satellites errors, propagation errors, station environment errors and so on. Meanwhile, un-modeling errors also have adverse effects. These errors not only undermine the assumption estimation criteria, and some non-white noises are likely to be absorbed by state parameters. In result, the accuracy of estimates is influenced. This paper regards white noise, colored noise and un-modeling errors as ZD stochastic model of GPS. Then the Allan variance method is proposed to analyze the posteriori residuals which can represent the Stochastic characteristics of GPS data. Noise component and parameters are mainly investigated. The result shows GPS noise behaves as WN plus GM. The phase and pseudorange WN is 2.392 mm and 0.936 m respectively, GM process noise is 4.450 mm/√s and 0.833 m/√s respectively, correlation time is 52.074 s and 14.737 s respectively. It is found that the phase GM component is associated with satellite, but the rest is associated with station. A number of analysis indicate that the ZD stochastic model characteristics of GPS obeys non-Gaussian white noise distribution and is to be refined.

Key words： Allan variance; GPS observation; Stochastic model; noise component; noise parameter; posteriori residuals

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