Paper Info
Title
A Refined SNR Based Stochastic Model to Reduce Site-Dependent Effects.
Abstract
Site-dependent effects are now the key factors that restrict the high accuracy applications of Global Navigation Satellite System (GNSS) technology, such as deformation monitoring. To reduce the effects of non-line-of-sight (NLOS) signal and multipath, methods and models applied to both of the function model and stochastic model of least-squares (LS) have been proposed. However, the existing methods and models may not be convenient to use and not be appropriate to all GNSS satellites. In this study, the SNR features of GPS and GLONASS are analyzed first, and a refined SNR based stochastic model is proposed, in which the links between carrier phase precision and SNR observation have been reasonably established. Compared with the existing models, the refined model in this paper could be used in real-time and the carrier phase precision could be reasonably shown with the SNR data. More importantly, it is applicable to all GNSS satellite systems. Based on this model, the site observation environment can be assessed in advance to show the obstruction area. With a bridge deformation monitoring platform, the performance of this model was tested in the aspect of integer ambiguity resolution and data processing. The results show that, compared with the existing stochastic models, this model could have the highest integer ambiguity resolution success rate and the lowest noise level in the data processing time series with obvious obstruction beside the site.
Year
DOI
Venue
2020
10.3390/rs12030493
REMOTE SENSING
Keywords
Field
DocType
site-dependent effects,SNR based stochastic model,GPS,GLONASS,ambiguity resolution,noise reduction
Computer vision,Algorithm,Artificial intelligence,Stochastic modelling,Geology
Journal
Volume
Issue
Citations 
12
3
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Ruijie Xi101.35
Xiaolin Meng24912.86
Weiping Jiang3810.55
Xiangdong An46413.56
Qiyi He501.01
Qusen Chen601.35