Massive GNSS Network Analysis Without Baselines: Undifferenced Ambiguity Resolution
作者: *Geng, Jianghui; Mao, Shuyin
来源出版物: JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH 卷: 126 期: 10 文献号: e2020JB021558 DOI: 10.1029/2020JB021558 出版年: OCT 2021
摘要: Integer ambiguity resolution (IAR) plays a key role in high-precision Global Navigation Satellite System (GNSS). While GNSS network analysis is usually achieved through double-difference (DD) IAR, undifferenced (UD) IAR has also been developed in recent years to achieve network analysis station by station. It is normally acquiesced in that DD-IAR and UD-IAR should achieve equivalent results, which is however not the case in a number of studies. We investigated four representatives DD-IAR and UD-IAR strategies using 1 year of GPS data from 192 globally distributed stations, where the position repeatability and the root mean squares error (RMSE) against the International GNSS Service weekly solutions were both quantified. We found that the position repeatability and the RMSE of DD-IAR were both worse than those of UD-IAR at over 90% of stations for the east component, though the largest deteriorations were only 0.6 and 0.7 mm, respectively. We demonstrate that it is the incorrectly resolved DD ambiguities, though accounting for far less than 1%, that degrade DD-IAR. Identifying such problematic ambiguities requires sophisticated quality control for DD-IAR, because the integer offset of an incorrect DD ambiguity might have been absorbed evenly by the cross-connected mass of DD ambiguities throughout the network, and consequently does not manifest as an outlier in the ambiguity-fixed solution. We, therefore, recommend UD-IAR for GNSS network analysis since it is immune to the error propagation resulting from incorrect integer ambiguities at other stations and thus more efficient to achieve ambiguity-fixed solutions.