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GNSS观测值域增强PPP定位中综合改正数质量控制算法
引用本文:洪菊,涂锐,张世旋,张鹏飞,王思遥,李芳馨,刘明玥.GNSS观测值域增强PPP定位中综合改正数质量控制算法[J].全球定位系统,2022,47(6):18-24.
作者姓名:洪菊  涂锐  张世旋  张鹏飞  王思遥  李芳馨  刘明玥
作者单位:1.中国科学院国家授时中心, 西安 710600
基金项目:国家自然科学基金项目(41974032)
摘    要:若全球卫星导航系统(GNSS)观测值域综合改正数中存在粗差或周跳则不可避免地会影响精密单点定位(PPP)增强的定位结果. 针对综合改正数中可能存在的异常,对综合改正数质量控制方法开展研究并提出异常识别与控制算法. 该算法根据综合改正数特点,利用经过频间和二阶历元间差分后的综合改正数组合值采用中位数法进行异常识别与定位,并对使用该异常值的卫星采用模糊度重新初始化、降权或剔除方法进行控制处理,以减少异常值对结果的影响. 以平均边长为26 km的部分香港连续运行参考站(CORS)组成的参考网以及科廷大学提供的零基线数据对该方法验证,结果表明:对30 s采样间隔的数据该方法能够有效探测出差分组合值中大部分1周以上的较大异常和部分1周以内的异常,有效控制部分异常值对定位结果的影响. 

关 键 词:全球卫星导航系统(GNSS)    区域增强精密单点定位(PPP)    观测值域综合改正数    数据预处理    质量控制
收稿时间:2022-06-07

The quality control algorithm of comprehensive corrections in augmented PPP algorithm of observation domain
Affiliation:1.Nation Time Service Center, Chinese Academy of Sciences, Xi’an 710600, China2.University of Chinese Academy of Sciences, Beijing 100049, China3.Key Laboratory of Precision Navigation and Timing Technology, Chinese Academy of Sciences, Xi’an 710600, China
Abstract:If there are gross errors or cycle slips in the Global Navigation Satellite System (GNSS) comprehensive corrections in observation space representation, it will inevitably affect precise point positioning (PPP) augmented performance. Aiming at the possible anomalies in the comprehensive corrections, a quality control algorithm for the comprehensive corrections was studied and an algorithm for identifying and controlling outliers was proposed. According to the characteristics of the comprehensive corrections, the median absolute deviation was used to identify and locate the outliers for the combined value of comprehensive corrections after inter-frequency difference and second-order inter-epoch. The satellites with outliers used were control by the means of ambiguity reinitialization, weight reduction or elimination to reduce the impact of outliers on the results. The method was validated with a reference network composed of some Hong Kong continuously operating reference stations (CORS) with an average side length of 26 km and zero baseline data from Curtin University. The results showed that the method can effectively detect most of the large anomalies more than 1 cycle and some anomalies within 1 cycle n the value of the differential combination, and effectively control the influence of some outliers on the positioning results. 
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