On the probability distribution of GNSS carrier phase observations
On the probability distribution of GNSS carrier phase observations
复制标题
DOI：
10.1007/s10291-010-0196-2
复制
发表时间：
2011-10
期刊：
影响因子：
4.9
通讯作者：
Xiaoguang Luo;M. Mayer;B. Heck
中科院分区：
文献类型：
--
作者：
Xiaoguang Luo;M. Mayer;B. Heck
当处理来自全球导航卫星系统（GNSS）的观测数据时，通常假设载波相位测量值遵循正态分布。虽然参数估计并不需要充分了解观测量的概率分布，例如在使用最小二乘法时，但全球导航卫星系统观测的分布特性在质量控制程序中发挥着关键作用，如异常值和周跳检测、模糊度解决以及估计结果的可靠性评估。此外，当在多路径和大气影响方面的关键观测条件下应用全球导航卫星系统定位时，全球导航卫星系统可观测量的正态分布假设的有效性肯定会受到质疑。本文阐述了正态分布假设和现实之间的差异，基于一个大的和有代表性的数据集的GPS相位测量涵盖了一系列因素，包括多径影响，基线长度，和大气条件。统计推断是使用第一到第四个样本矩，假设检验和图形工具，如直方图和分位数-分位数图。结果表明，多径效应，特别是近场分量，产生的GNSS观测量的分布特性的主要影响。此外，使用地面气象数据，相当大的相关性之间的分布偏离正常的一方面和大气相对湿度的其他检测。
When processing observational data from global navigation satellite systems (GNSS), the carrier phase measurements are generally assumed to follow a normal distribution. Although full knowledge of the probability distribution of the observables is not required for parameter estimation, for example when using the least-squares method, the distributional properties of GNSS observations play a key role in quality control procedures, such as outlier and cycle-slip detection, in ambiguity resolution, as well as in the reliability assessment of estimation results. In addition, when applying GNSS positioning under critical observation conditions with respect to multipath and atmospheric effects, the validity of the normal distribution assumption of GNSS observables certainly comes into doubt. This paper illustrates the discrepancies between the normal distribution assumption and reality, based on a large and representative data set of GPS phase measurements covering a range of factors, including multipath impact, baseline length, and atmospheric conditions. The statistical inferences are made using the first through fourth sample moments, hypothesis tests, and graphical tools such as histograms and quantile–quantile plots. The results show clearly that multipath effects, in particular the near-field component, produce the dominant influence on the distributional characteristics of GNSS observables. Additionally, using surface meteorological data, considerable correlations between distributional deviations from normality on the one hand and atmospheric relative humidity on the other are detected.