Modeling DGNSS Pseudo-Range Correction Messages by Utilizing Satellite Repeat Time

We developed and validated a pseudo-range correction (PRC) modeling system that can prevent degradation of positioning accuracy even in situations where one cannot obtain PRC messages for Differential Global Navigation Satellite System (DGNSS). A PRC modeling scheme was devised based on the repeat time of GNSS satellites and previously-collected PRC data. The difference between the modeled and real PRC values observed at the reference station showed a bias error of about ±1.0 m and a root mean square error (RMSE) less than 1.5 m. When we applied the predicted PRC to Differential Global Positioning System (DGPS) and Differential BeiDou (DBDS) positioning, horizontal RMSE values were at a level of 1.0 m, while vertical RMSE was in the range of 1.8–3.0 m. We found that modelled PRCs can provide positioning results similar to those based on real PRCs and can provide significant improvement over standalone positioning without PRCs.

[1]  Oliver Montenbruck,et al.  Satellite Orbits: Models, Methods and Applications , 2000 .

[2]  D. Gebre‐Egziabher,et al.  GNSS Applications and Methods , 2009 .

[3]  Young Jae Lee,et al.  Artificial neural networks for predicting DGPS carrier phase and pseudorange correction , 2008 .

[4]  Dominique Gruyer,et al.  A Novel Approach for Improved Vehicular Positioning Using Cooperative Map Matching and Dynamic Base Station DGPS Concept , 2016, IEEE Transactions on Intelligent Transportation Systems.

[5]  Penina Axelrad,et al.  Modified sidereal filtering: Implications for high‐rate GPS positioning , 2004 .

[6]  Jin Hyung Kim,et al.  Experimental studies of autonomous driving of a vehicle on the road using LiDAR and DGPS , 2015, 2015 15th International Conference on Control, Automation and Systems (ICCAS).

[7]  James L. Davis,et al.  Site-specific multipath characteristics of global IGS and CORS GPS sites , 2004 .

[8]  Dah-Jing Jwo,et al.  ARMA Neural Networks for Predicting DGPS Pseudorange Correction , 2004, Journal of Navigation.

[9]  Bao Shu,et al.  Accounting for Inter-System Bias in DGNSS Positioning with GPS/GLONASS/BDS/Galileo , 2017, Journal of Navigation.

[10]  Agus Budiyono,et al.  Principles of GNSS, Inertial, and Multi-sensor Integrated Navigation Systems , 2012 .

[11]  Klemen Kozmus Trajkovski,et al.  Sturdy Positioning with High Sensitivity GPS Sensors Under Adverse Conditions , 2010, Sensors.

[12]  D. Agnew,et al.  Finding the repeat times of the GPS constellation , 2006 .

[13]  Pawel Wielgosz,et al.  Selected properties of GPS and Galileo-IOV receiver intersystem biases in multi-GNSS data processing , 2015 .

[14]  Feng Zhu,et al.  New optimal smoothing scheme for improving relative and absolute accuracy of tightly coupled GNSS/SINS integration , 2017, GPS Solutions.

[15]  A. El-Rabbany Introduction to GPS: The Global Positioning System , 2002 .

[16]  Bernhard Hofmann-Wellenhof,et al.  GNSS - Global Navigation Satellite Systems: GPS, GLONASS, Galileo, and more , 2007 .

[17]  S. Kamijo,et al.  Autonomous driving positioning using building model and DGNSS , 2016, 2016 European Navigation Conference (ENC).

[18]  Yassine Ruichek,et al.  Fisheye-Based Method for GPS Localization Improvement in Unknown Semi-Obstructed Areas , 2017, Sensors.

[19]  Mohammad Reza Mosavi Comparing DGPS corrections prediction using neural network, fuzzy neural network, and Kalman filter , 2006 .

[20]  Pawel Wielgosz,et al.  Investigation of some selected strategies for multi-GNSS instantaneous RTK positioning , 2017 .