Use of modified carrier-to-code leveling to analyze temperature dependence of multi-GNSS receiver DCB and to retrieve ionospheric TEC

Deriving the ionospheric total electron content (TEC) from the global navigation satellite systems (GNSS) measurements typically assumes the receiver differential code biases (RDCBs) to remain unchanged within at least 1 day. However, the RDCBs sometimes can exhibit remarkable intraday variability, probably due to the ambient temperature fluctuation. The modified carrier-to-code leveling (MCCL) method enables one to eliminate the adverse impact of the short-term variations of RDCBs (called RDCB offsets) on the retrieval of ionospheric TECs. In this study, we extend the GPS-only MCCL method to the multi-GNSS case and further carry out a series of investigations. First, in terms of the Pearson correlation coefficient (PCC), the dependence of multi-GNSS RDCB offsets upon ambient temperature is verified. As suggested by the results, a strong linear correlation exists between the estimated RDCB offsets and measured temperature values. The percentages of the stations analyzed with the absolute PCC values above 0.5 are 76.5%, 94.1% and 64.2% for GPS, BDS and Galileo, respectively. Second, the global ionospheric map provided by the center for orbit determination in Europe (CODE), the JASON altimeter and the difference of slant TEC (dSTEC) are chosen as the references for evaluating the performance of MCCL-derived TECs. After removing the significant RDCB offsets, an improvement of 69.7%, 93.4% and 87.6% for GPS, BDS and Galileo has been achieved in the dSTEC validation, respectively.

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

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

[3]  Oliver Montenbruck,et al.  The IGS MGEX Experiment as a Milestone for a Comprehensive Multi-GNSS Service , 2013 .

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

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

[6]  S. Kao,et al.  Factors affecting the estimation of GPS receiver instrumental biases , 2013 .

[7]  Yunbin Yuan,et al.  A generalized trigonometric series function model for determining ionospheric delay , 2004 .

[8]  Baocheng Zhang,et al.  Determination of the optimized single-layer ionospheric height for electron content measurements over China , 2018, Journal of Geodesy.

[9]  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 .

[10]  Richard B. Langley,et al.  Defining the Basis of an Integer-Levelling Procedure for Estimating Slant Total Electron Content , 2011 .

[11]  Baocheng Zhang,et al.  Joint estimation of vertical total electron content (VTEC) and satellite differential code biases (SDCBs) using low-cost receivers , 2018, Journal of Geodesy.

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

[13]  Chuang Shi,et al.  Consistency of seven different GNSS global ionospheric mapping techniques during one solar cycle , 2018, Journal of Geodesy.

[14]  Wei Zhang,et al.  The variation of the estimated GPS instrumental bias and its possible connection with ionospheric variability , 2014 .

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

[16]  Ahmed El-Rabbany,et al.  MGR-DCB: A Precise Model for Multi-Constellation GNSS Receiver Differential Code Bias , 2015, Journal of Navigation.

[17]  K. Mikula,et al.  Numerical solution to the oblique derivative boundary value problem on non-uniform grids above the Earth topography , 2017, Journal of Geodesy.

[18]  Ahmed El-Rabbany,et al.  An efficient regional ionospheric model using combined GPS/BeiDou observations , 2017 .

[19]  Baocheng Zhang,et al.  A modified carrier-to-code leveling method for retrieving ionospheric observables and detecting short-term temporal variability of receiver differential code biases , 2018, Journal of Geodesy.

[20]  Baocheng Zhang,et al.  Real-Time Precise Point Positioning (RTPPP) with raw observations and its application in real-time regional ionospheric VTEC modeling , 2018, Journal of Geodesy.

[21]  P. Teunissen Zero Order Design: Generalized Inverses, Adjustment, the Datum Problem and S-Transformations , 1985 .

[22]  Andrzej Krankowski,et al.  Methodology and consistency of slant and vertical assessments for ionospheric electron content models , 2017, Journal of Geodesy.

[23]  Keke Zhang,et al.  Global Ionospheric Modelling using Multi-GNSS: BeiDou, Galileo, GLONASS and GPS , 2016, Scientific Reports.

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

[25]  Peter J. G. Teunissen A-PPP: Array-Aided Precise Point Positioning With Global Navigation Satellite Systems , 2012, IEEE Transactions on Signal Processing.

[26]  B. Wilson,et al.  A New Method for Monitoring the Earth's Ionospheric Total Electron Content Using the GPS Global Network , 1993 .

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

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

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

[30]  O. Montenbruck,et al.  Springer Handbook of Global Navigation Satellite Systems , 2017 .

[31]  Ningbo Wang,et al.  Estimation and analysis of the short-term variations of multi-GNSS receiver differential code biases using global ionosphere maps , 2018, Journal of Geodesy.

[32]  Yidong Lou,et al.  Modeling regional ionospheric delay with ground-based BeiDou and GPS observations in China , 2015, GPS Solutions.