GPS Satellite Orbit Prediction at User End for Real-Time PPP System

This paper proposed the high-precision satellite orbit prediction process at the user end for the real-time precise point positioning (PPP) system. Firstly, the structure of a new real-time PPP system will be briefly introduced in the paper. Then, the generation of satellite initial parameters (IP)...

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Main Authors: Hongzhou Yang, Yang Gao
Format: Article
Language:English
Published: MDPI AG 2017-08-01
Series:Sensors
Subjects:
Online Access:https://www.mdpi.com/1424-8220/17/9/1981
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spelling doaj-a3b1c583e0a3421486c8a666209de7bf2020-11-24T23:23:53ZengMDPI AGSensors1424-82202017-08-01179198110.3390/s17091981s17091981GPS Satellite Orbit Prediction at User End for Real-Time PPP SystemHongzhou Yang0Yang Gao1Profound Positioning Inc., Calgary, AB T2P 3G3, CanadaDepartment of Geomatics, University of Calgary, Calgary, AB T2N 1N4, CanadaThis paper proposed the high-precision satellite orbit prediction process at the user end for the real-time precise point positioning (PPP) system. Firstly, the structure of a new real-time PPP system will be briefly introduced in the paper. Then, the generation of satellite initial parameters (IP) at the sever end will be discussed, which includes the satellite position, velocity, and the solar radiation pressure (SRP) parameters for each satellite. After that, the method for orbit prediction at the user end, with dynamic models including the Earth’s gravitational force, lunar gravitational force, solar gravitational force, and the SRP, are presented. For numerical integration, both the single-step Runge–Kutta and multi-step Adams–Bashforth–Moulton integrator methods are implemented. Then, the comparison between the predicted orbit and the international global navigation satellite system (GNSS) service (IGS) final products are carried out. The results show that the prediction accuracy can be maintained for several hours, and the average prediction error of the 31 satellites are 0.031, 0.032, and 0.033 m for the radial, along-track and cross-track directions over 12 h, respectively. Finally, the PPP in both static and kinematic modes are carried out to verify the accuracy of the predicted satellite orbit. The average root mean square error (RMSE) for the static PPP of the 32 globally distributed IGS stations are 0.012, 0.015, and 0.021 m for the north, east, and vertical directions, respectively; while the RMSE of the kinematic PPP with the predicted orbit are 0.031, 0.069, and 0.167 m in the north, east and vertical directions, respectively.https://www.mdpi.com/1424-8220/17/9/1981real-time PPPorbit predictioninitial parametersnumerical integrationuser end
collection DOAJ
language English
format Article
sources DOAJ
author Hongzhou Yang
Yang Gao
spellingShingle Hongzhou Yang
Yang Gao
GPS Satellite Orbit Prediction at User End for Real-Time PPP System
Sensors
real-time PPP
orbit prediction
initial parameters
numerical integration
user end
author_facet Hongzhou Yang
Yang Gao
author_sort Hongzhou Yang
title GPS Satellite Orbit Prediction at User End for Real-Time PPP System
title_short GPS Satellite Orbit Prediction at User End for Real-Time PPP System
title_full GPS Satellite Orbit Prediction at User End for Real-Time PPP System
title_fullStr GPS Satellite Orbit Prediction at User End for Real-Time PPP System
title_full_unstemmed GPS Satellite Orbit Prediction at User End for Real-Time PPP System
title_sort gps satellite orbit prediction at user end for real-time ppp system
publisher MDPI AG
series Sensors
issn 1424-8220
publishDate 2017-08-01
description This paper proposed the high-precision satellite orbit prediction process at the user end for the real-time precise point positioning (PPP) system. Firstly, the structure of a new real-time PPP system will be briefly introduced in the paper. Then, the generation of satellite initial parameters (IP) at the sever end will be discussed, which includes the satellite position, velocity, and the solar radiation pressure (SRP) parameters for each satellite. After that, the method for orbit prediction at the user end, with dynamic models including the Earth’s gravitational force, lunar gravitational force, solar gravitational force, and the SRP, are presented. For numerical integration, both the single-step Runge–Kutta and multi-step Adams–Bashforth–Moulton integrator methods are implemented. Then, the comparison between the predicted orbit and the international global navigation satellite system (GNSS) service (IGS) final products are carried out. The results show that the prediction accuracy can be maintained for several hours, and the average prediction error of the 31 satellites are 0.031, 0.032, and 0.033 m for the radial, along-track and cross-track directions over 12 h, respectively. Finally, the PPP in both static and kinematic modes are carried out to verify the accuracy of the predicted satellite orbit. The average root mean square error (RMSE) for the static PPP of the 32 globally distributed IGS stations are 0.012, 0.015, and 0.021 m for the north, east, and vertical directions, respectively; while the RMSE of the kinematic PPP with the predicted orbit are 0.031, 0.069, and 0.167 m in the north, east and vertical directions, respectively.
topic real-time PPP
orbit prediction
initial parameters
numerical integration
user end
url https://www.mdpi.com/1424-8220/17/9/1981
work_keys_str_mv AT hongzhouyang gpssatelliteorbitpredictionatuserendforrealtimepppsystem
AT yanggao gpssatelliteorbitpredictionatuserendforrealtimepppsystem
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