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An Enhanced Multi-GNSS Navigation Algorithm by Utilising a Priori Inter-System Biases

Published online by Cambridge University Press:  18 September 2017

Zhounan Dong
Affiliation:
(School of Geosciences and Info-Physics, Central South University, Changsha 410083, China)
Changsheng Cai*
Affiliation:
(School of Geosciences and Info-Physics, Central South University, Changsha 410083, China)
Rock Santerre
Affiliation:
(Département des Sciences Géomatiques, Université Laval, Québec G1V 0A6, Canada)
Cuilin Kuang
Affiliation:
(School of Geosciences and Info-Physics, Central South University, Changsha 410083, China)
*

Abstract

The integration of multi-constellation Global Navigation Satellite System (GNSS) measurements can effectively improve the accuracy and reliability of navigation and positioning solutions, while the Inter-System Bias (ISB) is a key issue for compatibility. The ISB is traditionally estimated as an unknown parameter along with three-dimensional position coordinates and a receiver clock offset with respect to Global Positioning System (GPS) time. ISB estimation of this sort will sacrifice a satellite observation for each integrated GNSS system. These sacrificed observations could be vital in situations of limited satellite visibility. In this study, an enhanced multi-GNSS navigation algorithm is developed to avoid sacrificing observations under poor visibility conditions. The main idea of this algorithm is to employ a moving average filter to smooth the ISBs estimated at previous epochs. The filtered value is utilised as a priori information at the current epoch. Experimental tests were conducted to evaluate the enhanced algorithm under open and blocked sky conditions. The results show that the enhanced algorithm effectively improves the accuracy and availability of navigation solutions under the blocked sky condition, with performance being comparable to traditional ISB estimation algorithms in open sky conditions. The improvement rates of the three-dimensional position accuracy and availability reach up to 63% and 21% in the blocked sky environment. Even in the case of only four different GNSS satellites, a position solution can still be obtained using the enhanced algorithm.

Type
Research Article
Copyright
Copyright © The Royal Institute of Navigation 2017 

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References

REFERENCES

Angrisano, A., Gaglione, S. and Gioia, C. (2013). Performance Assessment of GPS/GLONASS Single Point Positioning in an Urban Environment. Acta Geodaetica et Geophysica, 48(2), 149161.Google Scholar
Bogdanov, P.P., Druzhin, A.V., Primakina, T.V. and Tiuliakov, A.E. (2015). System Time Synchronization for GNSS Interoperability. Coordinates, XI(9), Available at: http://mycoordinates.org/system-time-synchronization-for-gnss-interoperability/.Google Scholar
Cai, C. and Gao, Y. (2008). Estimation of GPS/GLONASS System Time Difference with Application to PPP. Proceedings of ION GNSS 2008, September 16–19, Savannah, Georgia,USA, 28802887.Google Scholar
Cai, C. and Gao, Y. (2009). A Combined GPS/GLONASS Navigation Algorithm for Use with Limited Satellite Visibility. Journal of Navigation, 62(4), 671685.CrossRefGoogle Scholar
Cai, C. and Gao, Y. (2013). Modelling and Assessment of Combined GPS/GLONASS Precise Point Positioning. GPS Solutions, 17(2), 223236.Google Scholar
Cai, C., Gao, Y., Pan, L. and Zhu, J. (2015). Precise Point Positioning with Quad-Constellations: GPS, BeiDou, GLONASS and Galileo. Advances in Space Research, 56(1), 133143.Google Scholar
Cai, C., Luo, X., Liu, Z. and Xiao, Q. (2014). Galileo Signal and Positioning Performance Analysis Based on Four IOV Satellites. Journal of Navigation, 67(5), 810824.Google Scholar
Chen, J., Xiao, P., Zhang, Y. and Wu, B. (2013). GPS/GLONASS System Bias Estimation and Application in GPS/GLONASS Combined Positioning. China Satellite Navigation Conference (CSNC) 2013 Proceedings, May 15–17, Wuhan, Hubei, China, 323333.Google Scholar
Choi, B.K., Cho, C.H., Cho, J.H. and Lee, S.J. (2015). Multi-GNSS Standard Point Positioning Using GPS, GLONASS, BeiDou and QZSS Measurements Recorded at MKPO Reference Station in South Korea. Journal of Positioning, Navigation, and Timing, 4(4), 205211.Google Scholar
Gioia, C. and Borio, D. (2016). A Statistical Characterization of the Galileo-to-GPS Inter-system Bias. Journal of Geodesy, 90(11), 12791291.Google Scholar
IDC-BDS. (2012). BeiDou Navigation Satellite System Signal In Space Interface Control Document – Open Service Signal B1I. Version 1.0, China Satellite Navigation Office.Google Scholar
IDC-GLONASS. (2008). Glonass Interface Control Document. Version 5.1, Russian Institute of Space Device Engineering: Moscow, Russia.Google Scholar
ICD-GPS. (2013). Global Positioning Systems Directorate System Engineering & Integration Interface Specification IS-GPS-200G. Navstar GPS Space Segment/Navigation User Interfaces.Google Scholar
Jiang, N., Xu, Y., Xu, T., Xu, G., Sun, Z. and Schuh, H. (2017). GPS/BDS Short-term ISB Modelling and Prediction. GPS Solutions, 21(1), 163175.Google Scholar
Jin, S., Feng, G.P. and Gleason, S. (2011). Remote sensing using GNSS signals: Current Status and Future Directions. Advances in Space Research, 47(10), 16451653.Google Scholar
Jin, S., Jin, R. and Li, D. (2016). Assessment of BeiDou Differential Code Bias Variations from Multi-GNSS Network Observations. Annales Geophysicae, 34(2), 259269.Google Scholar
Jin, S., Qian, X. and Wu, X. (2017). Sea Level Change from BeiDou Navigation Satellite System-Reflectometry (BDS-R): First Results and Evaluation. Global and Planetary Change, 149, 2025, doi: 10.1016/j.gloplacha.2016.12.010.Google Scholar
Klobuchar, J.A. (1987). Ionospheric Time-delay Algorithm for Single-frequency GPS Users. IEEE Transactions on Aerospace and Electronic Systems, AES-23(3), 325331.Google Scholar
Montenbruck, O., Steigenberger, P. and Hauschild, A. (2015). Broadcast Versus Precise Ephemerides: a Multi-GNSS Perspective. GPS Solutions, 19(2), 321333.Google Scholar
Nava, B., Coisson, P. and Radicella, S.M. (2008). A New Version of the NeQuick Ionosphere Electron Density Model. Journal of Atmospheric and Solar-Terrestrial Physics, 70(15), 18561862.Google Scholar
Oladipo, O.A. and Schüler, T. (2012). GNSS Single Frequency Ionospheric Range Delay Corrections: NeQuick Data Ingestion Technique. Advances in Space Research, 50(9), 12041212.CrossRefGoogle Scholar
Pan, L., Cai, C., Santerre, R. and Zhang, X. (2016). Performance Evaluation of Single-frequency Point Positioning with GPS, GLONASS, BeiDou and Galileo. Survey Review, doi 10.1007/s10291-015-0513-x, 2016.Google Scholar
Saastamoinen, J. (1973). Contributions to the Theory of Atmospheric Refraction. Bulletin Géodésique (1946–1975), 107(1), 1334.CrossRefGoogle Scholar
Torre, A.D. and Caporali, A. (2015). An Analysis of Intersystem Biases for Multi-GNSS Positioning. GPS Solutions, 19(2), 297307.Google Scholar