局地大气条件下多参考站环境对流层异常探测——(1):异常探测算法的基本概念

Y. Yoo
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引用次数: 0

摘要

极端的对流层异常,如台风或区域性暴雨,会降低GPS信号的定位精度。它成为影响网络RTK中高精度定位方案的主要误差项之一。本文提出了一种用于大气异常期间的检测算法,用于检测多参考站间大气条件不均匀导致的网络误差所导致的对流层异常,从而降低校正数据的质量。它使用由四个气象站组成的大气网格,在大气网格的低性能点估计对流层天顶总延迟差。自动气象站(AWS)的气象数据可以应用于所提出的对流层异常检测算法,当台站之间存在不同的大气条件时。提出了delta对流层倾斜延迟概率密度分布的概念,用于确定阈值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Tropospheric Anomaly Detection in Multi-reference Stations Environment during Localized Atmosphere Conditions-(1) : Basic Concept of Anomaly Detection Algorithm
Extreme tropospheric anomalies such as typhoons or regional torrential rain can degrade positioning accuracy of the GPS signal. It becomes one of the main error terms affecting high-precision positioning solutions in network RTK. This paper proposed a detection algorithm to be used during atmospheric anomalies in order to detect the tropospheric irregularities that can degrade the quality of correction data due to network errors caused by inhomogeneous atmospheric conditions between multi-reference stations. It uses an atmospheric grid that consists of four meteorological stations and estimates the troposphere zenith total delay difference at a low performance point in an atmospheric grid. AWS (automatic weather station) meteorological data can be applied to the proposed tropospheric anomaly detection algorithm when there are different atmospheric conditions between the stations. The concept of probability density distribution of the delta troposphere slant delay was proposed for the threshold determination.
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