Analysing Time Series of GNSS Residuals by Means of AR(I)MA Processes
Analysing Time Series of GNSS Residuals by Means of AR(I)MA Processes
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DOI：
10.1007/978-3-642-22078-4_19
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发表时间：
2012
期刊：
影响因子：
--
通讯作者：
Xiaoguang Luo;M. Mayer;B. Heck
中科院分区：
文献类型：
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作者：
Xiaoguang Luo;M. Mayer;B. Heck
经典的最小二乘（LS）算法广泛应用于处理全球导航卫星系统（GNSS）的数据。然而，大多数 GNSS 处理软件产品都忽略了一些影响未知参数精度测量的限制因素，例如观测数据的时间相关性。为了研究GNSS观测值的时间相关性特征，本文引入自回归（集成）移动平均（AR（I）MA）过程来分析LS评估产生的残差时间序列。基于代表性数据库，研究了基线长度、多路径效应、观测加权、大气条件等各种因素对ARIMA识别的影响。此外，还比较了不同的时间相关模型，例如一阶 AR 过程、ARMA 过程和凭经验确定的分析自相关函数的模型适当性和效率。
The classical least-squares (LS) algorithm is widely applied in processing data from Global Navigation Satellite Systems (GNSS). However, some limiting factors impacting the accuracy measures of unknown parameters such as temporal correlations of observational data are neglected in most GNSS processing software products. In order to study the temporal correlation characteristics of GNSS observations, this paper introduces autoregressive (integrated) moving average (AR(I)MA) processes to analyse residual time series resulting from the LS evaluation. Based on a representative data base the influences of various factors, like baseline length, multipath effects, observation weighting, atmospheric conditions on ARIMA identification are investigated. Additionally, different temporal correlation models, for example first-order AR processes, ARMA processes, and empirically determined analytical autocorrelation functions are compared with respect to model appropriateness and efficiency.