A Refined SNR Based Stochastic Model to Reduce Site-Dependent Effects

Ruijie Xi,Qusen Chen,Xiaolin Meng,Xiangdong An,Qiyi He,Weiping Jiang

doi:10.3390/rs12030493

Ruijie Xi, Qusen Chen + Show 4 more

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https://doi.org/10.3390/rs12030493

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Journal: Remote sensing	Publication Date: Feb 4, 2020
Citations: 8	License type: CC BY 4.0

Affiliation: Wuhan University, University of Nottingham

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.

Highlights

Global Navigation Satellite Systems (GNSS) are gradually recognized as an essential tool in every aspect of geodesy and geodynamics
For the L2 phase observation, the Signal-to-Noise Ratio (SNR) value is clearly lower than L1
A refined SNR based stochastic model is proposed to weight the GNSS observations taking according of site-specific effects

Summary

Introduction

Global Navigation Satellite Systems (GNSS) are gradually recognized as an essential tool in every aspect of geodesy and geodynamics. In the application of high precision deformation monitoring, the relative double differential positioning technology with short baselines is widely applied, due to the advantages of eliminating satellite orbits errors, receiver and satellite clock offsets, and of reducing ionosphere and troposphere delays. It is a relatively easy way to process the data compared with the long baseline data processing. We know that the signal obstruction can mainly cause three essential problems They are, (1) multipath effects from signal reflection, (2) signal diffractions and (3) the satellite geometry strength reduction. Since the site-dependent effect is related to the observation environment at each station, it cannot be eliminated by double differencing so as to be an unresolved problem in the short baseline data processing [7,8]

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