2021Survey ReviewRequires access

Estimation of the ocean tide loading displacements by GPS and GLONASS kinematic Precise Point Positioning

Hong Zhao, Zhi Liu, Zhi Liu, Guangwei Jiang, Zhanke Liu, Zhanke Liu, Yangyang Sun, Dan He

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Abstract

Ocean Tide Loading Displacement (OTLD) parameters, derived by Global Positioning System (GPS) approach, need more time to converge to a stable state and show poor precision of K1 and K2 constituents. Combining multi-system Global Navigation Satellite System (GNSS) data can greatly improve the positioning accuracy and significantly reduce the convergence time compared to single GPS PPP, combined GPS+GLONASS with ambiguity resolved in kinematic Precise Point Positioning (PPP) is improved on the basis of the algorithm of GPS kinematic PPP. We collected eight years of continuous observations of 11 worldwide sites from International GNSS Service to generate three dimensional OTLD parameters of eight constituents using GPS with ambiguity fixed, GLONASS with ambiguity free and combined GPS+GLONASS with ambiguity fixed kinematic PPP approach. The results show the eight constituents derived by GPS_FX and GPS+GLONASS_FX solution need four years and three years to converge to stable values, respectively. GLONASS can significantly improve the convergence rate of K1 and K2 constituents which needs only two years to converge to stable values, and S2 and P1 constituents which need three years to stable values. The results also show the GPS+GLONASS/reference misfits are the smallest for the most constituents in three components (less than 2mm) except for K2 and K1 in vertical and north directions. GLONASS can improve more than 40% in vertical and north components over GPS and GPS+GLONASS for K2 and K1 constituents and show very close agreement with GPS_FX solution in vertical and north component for S2 and P1 constituents. Ambiguity resolved in OTLD estimation can significantly accelerate the convergence and improve the accuracy for eight constituents, especially for combined GPS+GLONASS kinematic PPP mode, but no single constellation mode solution for all eight major constituents exists.

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What this paper is about

Ocean Tide Loading Displacement (OTLD) parameters, derived by Global Positioning System (GPS) approach, need more time to converge to a stable state and show poor precision of K1 and K2 constituents. Combining multi-system Global Navigation Satellite System (GNSS) data can greatly improve the positioning accuracy and significantly reduce the convergence time compared to single GPS PPP, combined GPS+GLONASS with ambiguity resolved in kinematic Precise Point Positioning (PPP) is improved on the basis of the algorithm of GPS kinematic PPP. We collected eight years of continuous observations of 11 worldwide sites from International GNSS Service to generate three dimensional OTLD parameters of eight constituents using GPS with ambiguity fixed, GLONASS with ambiguity free and combined GPS+GLONASS with ambiguity fixed kinematic PPP approach. The results show the eight constituents derived by GPS_FX and GPS+GLONASS_FX solution need four years and three years to converge to stable values, respectively. GLONASS can significantly improve the convergence rate of K1 and K2 constituents which needs only two years to converge to stable values, and S2 and P1 constituents which need three years to stable values. The results also show the GPS+GLONASS/reference misfits are the smallest for the most constituents in three components (less than 2mm) except for K2 and K1 in vertical and north directions. GLONASS can improve more than 40% in vertical and north components over GPS and GPS+GLONASS for K2 and K1 constituents and show very close agreement with GPS_FX solution in vertical and north component for S2 and P1 constituents. Ambiguity resolved in OTLD estimation can significantly accelerate the convergence and improve the accuracy for eight constituents, especially for combined GPS+GLONASS kinematic PPP mode, but no single constellation mode solution for all eight major constituents exists.

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Available abstract

Ocean Tide Loading Displacement (OTLD) parameters, derived by Global Positioning System (GPS) approach, need more time to converge to a stable state and show poor precision of K1 and K2 constituents. Combining multi-system Global Navigation Satellite System (GNSS) data can greatly improve the positioning accuracy and significantly reduce the convergence time compared to single GPS PPP, combined GPS+GLONASS with ambiguity resolved in kinematic Precise Point Positioning (PPP) is improved on the basis of the algorithm of GPS kinematic PPP. We collected eight years of continuous observations of 11 worldwide sites from International GNSS Service to generate three dimensional OTLD parameters of eight constituents using GPS with ambiguity fixed, GLONASS with ambiguity free and combined GPS+GLONASS with ambiguity fixed kinematic PPP approach. The results show the eight constituents derived by GPS_FX and GPS+GLONASS_FX solution need four years and three years to converge to stable values, respectively. GLONASS can significantly improve the convergence rate of K1 and K2 constituents which needs only two years to converge to stable values, and S2 and P1 constituents which need three years to stable values. The results also show the GPS+GLONASS/reference misfits are the smallest for the most constituents in three components (less than 2mm) except for K2 and K1 in vertical and north directions. GLONASS can improve more than 40% in vertical and north components over GPS and GPS+GLONASS for K2 and K1 constituents and show very close agreement with GPS_FX solution in vertical and north component for S2 and P1 constituents. Ambiguity resolved in OTLD estimation can significantly accelerate the convergence and improve the accuracy for eight constituents, especially for combined GPS+GLONASS kinematic PPP mode, but no single constellation mode solution for all eight major constituents exists.

Key concepts: GLONASS, Global Positioning System, Precise Point Positioning, GNSS applications, Real Time Kinematic, Geodesy, Kinematics, Computer science

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