Consistency and analysis of ionospheric observables obtained from three precise point positioning models

Ionospheric observables based on Global Navigation Satellite System can be obtained by a variety of approaches. The most widely used one is the geometry-free combination of carrier-phase smoothed code measurements. This method, however, introduces leveling errors that substantially degrade the performance of ionospheric modeling and bias estimation. To reduce leveling errors, precise point positioning (PPP) model is preferred for obtaining the ionospheric observables. We aim to investigate whether the ionospheric observables obtained from three different PPP models are consistent and how the PPP-based ionospheric observables relates to the smoothed code method. The paper begins by formulating the ionospheric observables. We then explain the statistical evaluation methods used for analyzing the bias terms derived from these methods and assessing the leveling errors from the carrier-phase smoothed code method. Numerical analysis is then conducted to compare the bias terms in the ionospheric observables and evaluate the leveling errors. The ionospheric observables based on the three PPP models show strong consistency. Compared to leveling errors in the carrier-phase smoothed code method, the leveling errors using the uncombined PPP model are significantly reduced up to five times.

[1]  Peter Steigenberger,et al.  Differential Code Bias Estimation using Multi‐GNSS Observations and Global Ionosphere Maps , 2014 .

[2]  Baocheng Zhang,et al.  Extraction of line-of-sight ionospheric observables from GPS data using precise point positioning , 2012, Science China Earth Sciences.

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

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

[5]  Baocheng Zhang,et al.  Zero-baseline Analysis of GPS/BeiDou/Galileo Between-Receiver Differential Code Biases (BR-DCBs): Time-wise Retrieval and Preliminary Characterization , 2016 .

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

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

[8]  Jaume Sanz,et al.  GPS differential code biases determination: methodology and analysis , 2017, GPS Solutions.

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

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

[11]  Xinan Yue,et al.  Is the long-term variation of the estimated GPS differential code biases associated with ionospheric variability? , 2016, GPS Solutions.

[12]  Yang Gao,et al.  Carrier phase-based ionospheric observables using PPP models , 2017 .

[13]  Nicolas Bergeot,et al.  Distribution and mitigation of higher‐order ionospheric effects on precise GNSS processing , 2014 .

[14]  Jim R. Ray,et al.  Geodetic techniques for time and frequency comparisons using GPS phase and code measurements , 2005 .

[15]  Allan T. Weatherwax,et al.  Accuracy of GPS total electron content: GPS receiver bias temperature dependence , 2013 .

[16]  Oliver Montenbruck,et al.  Determination of differential code biases with multi-GNSS observations , 2016, Journal of Geodesy.

[17]  L. Mervart,et al.  Bernese GPS Software Version 5.0 , 2007 .

[18]  Zishen Li,et al.  Two-step method for the determination of the differential code biases of COMPASS satellites , 2012, Journal of Geodesy.

[19]  Baocheng Zhang,et al.  On the short-term temporal variations of GNSS receiver differential phase biases , 2017, Journal of Geodesy.

[20]  J. Kouba,et al.  GPS Precise Point Positioning Using IGS Orbit Products , 2001 .

[21]  Ying Li,et al.  Estimation and analysis of Galileo differential code biases , 2017, Journal of Geodesy.

[22]  E. Sardón,et al.  Estimation of total electron content using GPS data: How stable are the differential satellite and receiver instrumental biases? , 1997 .

[23]  Baocheng Zhang,et al.  Three methods to retrieve slant total electron content measurements from ground‐based GPS receivers and performance assessment , 2016 .