Department of Transport and Logistics, Gdynia Maritime University, Morska 81-87, 81-225 Gdynia, Poland.
Sensors (Basel). 2020 Dec 23;21(1):31. doi: 10.3390/s21010031.
Positioning systems are used to determine position coordinates in navigation (air, land and marine). The accuracy of an object's position is described by the position error and a statistical analysis can determine its measures, which usually include: Root Mean Square (RMS), twice the Distance Root Mean Square (2DRMS), Circular Error Probable (CEP) and Spherical Probable Error (SEP). It is commonly assumed in navigation that position errors are random and that their distribution are consistent with the normal distribution. This assumption is based on the popularity of the Gauss distribution in science, the simplicity of calculating RMS values for 68% and 95% probabilities, as well as the intuitive perception of randomness in the statistics which this distribution reflects. It should be noted, however, that the necessary conditions for a random variable to be normally distributed include the independence of measurements and identical conditions of their realisation, which is not the case in the iterative method of determining successive positions, the filtration of coordinates or the dependence of the position error on meteorological conditions. In the preface to this publication, examples are provided which indicate that position errors in some navigation systems may not be consistent with the normal distribution. The subsequent section describes basic statistical tests for assessing the fit between the empirical and theoretical distributions (Anderson-Darling, chi-square and Kolmogorov-Smirnov). Next, statistical tests of the position error distributions of very long Differential Global Positioning System (DGPS) and European Geostationary Navigation Overlay Service (EGNOS) campaigns from different years (2006 and 2014) were performed with the number of measurements per session being 900'000 fixes. In addition, the paper discusses selected statistical distributions that fit the empirical measurement results better than the normal distribution. Research has shown that normal distribution is not the optimal statistical distribution to describe position errors of navigation systems. The distributions that describe navigation positioning system errors more accurately include: beta, gamma, logistic and lognormal distributions.
定位系统用于确定导航中的位置坐标(空中、陆地和海洋)。物体位置的精度由位置误差来描述,统计分析可以确定其度量值,通常包括:均方根 (RMS)、两倍距离均方根 (2DRMS)、圆概率误差 (CEP) 和球形概率误差 (SEP)。在导航中,通常假设位置误差是随机的,并且它们的分布与正态分布一致。这种假设基于高斯分布在科学中的普及性、计算 68%和 95%概率 RMS 值的简单性,以及该分布反映的随机性在统计学中的直观感知。然而,需要注意的是,随机变量正态分布的必要条件包括测量的独立性和其实现条件的一致性,而在确定连续位置的迭代方法、坐标滤波或位置误差对气象条件的依赖中,并不满足这些条件。在本出版物的前言中,提供了一些示例,这些示例表明在某些导航系统中,位置误差可能不符合正态分布。接下来的部分描述了基本的统计检验,用于评估经验分布和理论分布之间的拟合程度(Anderson-Darling、卡方和 Kolmogorov-Smirnov)。接下来,对来自不同年份(2006 年和 2014 年)的非常长的差分全球定位系统 (DGPS) 和欧洲静地导航重叠服务 (EGNOS) 测量的位置误差分布进行了统计检验,每次测量的会话数为 900'000 次。此外,本文还讨论了一些比正态分布更能拟合经验测量结果的选定统计分布。研究表明,正态分布不是描述导航系统位置误差的最优统计分布。更准确地描述导航定位系统误差的分布包括:贝塔分布、伽马分布、逻辑斯谛分布和对数正态分布。