Carrier phase-based ionospheric observables using PPP models

Abstract The ionosphere is one of the major error sources in Global Navigation Satellite System (GNSS) positioning, navigation and timing. Estimating the ionospheric delays precisely is of great interest in the GNSS community. To date, GNSS observables for ionospheric estimation are most commonly based on carrier phase smoothed code measurements. However, leveling errors, which affect the performance of ionospheric modeling and differential code bias (DCB) estimation, exist in the carrier phase smoothed code observations. Such leveling errors are caused by the multipath and the short-term variation of DCB. To reduce these leveling errors, this paper investigates and estimates the ionospheric delays based on carrier phase measurements without the leveling errors. The line-of-sight ionospheric observables with high precision are calculated using precise point positioning (PPP) techniques, in which carrier phase measurements are the principal observables. Ionosphere-free and UofC PPP models are applied and compared for their effectiveness to minimize the leveling errors. To assess the leveling errors, single difference of ionospheric observables for a short baseline is examined. Results show that carrier phase-derived ionospheric observables from PPP techniques can effectively reduce the leveling errors. Furthermore, we compared the PPP ionosphere estimation model with the conventional carrier phase smoothed code method to assess the bias consistency and investigate the biases in the ionospheric observables.

[1]  Yidong Lou,et al.  An improved approach to model ionospheric delays for single-frequency Precise Point Positioning , 2012 .

[2]  Richard B. Langley,et al.  Ionospheric Monitoring Using "Integer-Levelled" Observations , 2012 .

[3]  J. K. Ray,et al.  Mitigation of GPS code and carrier phase multipath effects using a multi-antenna system , 2000 .

[4]  S. Schaer Mapping and predicting the Earth's ionosphere using the Global Positioning System. , 1999 .

[5]  Zhizhao Liu,et al.  Ionosphere modelling using carrier smoothed ionosphere observations from a regional gps network , 2002 .

[6]  Guillermo Gonzalez-Casado,et al.  A Worldwide Ionospheric Model for Fast Precise Point Positioning , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[7]  Maorong Ge,et al.  A real-time ionospheric model based on GNSS Precise Point Positioning , 2013 .

[8]  Pierre Héroux,et al.  Precise Point Positioning Using IGS Orbit and Clock Products , 2001, GPS Solutions.

[9]  Anthony J. Mannucci,et al.  Automated daily processing of more than 1000 ground‐based GPS receivers for studying intense ionospheric storms , 2005 .

[10]  Attila Komjathy,et al.  Global ionospheric total electron content mapping using the global positioning system , 1997 .

[11]  M. Abdel-Salam,et al.  Precise point positioning using un-differenced code and carrier phase observations , 2005 .

[12]  Ningbo Wang,et al.  Analysis and validation of different global ionospheric maps (GIMs) over China , 2015 .

[13]  Manuel Hernández-Pajares,et al.  The ionosphere: effects, GPS modeling and the benefits for space geodetic techniques , 2011 .

[14]  Yang Gao,et al.  Improving Ambiguity Convergence in Carrier Phase-Based Precise Point Positioning , 2001 .

[15]  A. Garcia-Rigo,et al.  The IGS VTEC maps: a reliable source of ionospheric information since 1998 , 2009 .

[16]  Zhang Baocheng,et al.  Extraction of line-of-sight ionospheric observables from GPS data using precise point positioning , 2012 .

[17]  Sandro M. Radicella,et al.  Calibration errors on experimental slant total electron content (TEC) determined with GPS , 2007 .

[18]  Anthony J. Mannucci,et al.  A global mapping technique for GPS‐derived ionospheric total electron content measurements , 1998 .