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GNSS Receiver Autonomous Integrity Monitoring with a Dynamic Model

Published online by Cambridge University Press:  20 April 2007

Steve Hewitson  and
Jinling Wang
Steve Hewitson*
Affiliation:
(The University of New South Wales)
Jinling Wang
Affiliation:
(The University of New South Wales)
*
(Email: s.hewitson@student.unsw.edu.au)
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Abstract

Traditionally, GNSS receiver autonomous integrity monitoring (RAIM) has been based upon single epoch solutions. RAIM can be improved considerably when available dynamic information is fused together with the GNSS range measurements in a Kalman filter. However, while the Kalman filtering technique is widely accepted to provide optimal estimates for the navigation parameters of a dynamic platform, assuming the state and observation models are correct, it is still susceptible to unmodelled errors. Furthermore, significant deviations from the assumed models for dynamic systems may also occur. It is therefore necessary that the state estimation procedure is complemented with effective and reliable integrity measures capable of identifying both measurement and modelling errors. Within this paper, fundamental equations required for the effective detection and identification of outliers in a kinematic GNSS positioning and navigation system are described together with the reliability and separability measures. These quality measures are implemented using a Kalman filtering procedure formulated with Gauss-Markov models where the state estimates are derived from least squares principles. Detailed simulations and analyses have been performed to assess the impact of the dynamic information on GNSS RAIM with respect to outlier detection and identification, reliability and separability. The ability of the RAIM algorithms to detect and identify dynamic modelling errors is also investigated.

Keywords

GNSSReceiver Autonomous Integrity MonitoringDynamic Model
Type
Research Article
Information
The Journal of Navigation , Volume 60 , Issue 2 , May 2007 , pp. 247 - 263
Copyright
Copyright © The Royal Institute of Navigation 2007

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References

REFERENCES

Baarda, W. (1968) A testing procedure for use in geodetic networks, Netherlands Geodetic Commission, New Series, Vol. 2, No. 4.CrossRefGoogle Scholar
Brown, R. G. and Hwang, P. Y. C. (1996) Introduction to Random Signals and Applied Kalman Filtering, 3rd ed., Wiley, New York.Google Scholar
Cross, P. A., Hawksbee, D. J., & Nicolai, R. (1987) Kalman Filtering/Smoother Equations: Their Derivation and Implementation. Royal Institute of Chartered Surveyors/Hydrographic Society Seminar, University of Nottingham.Google Scholar
Cross, P. A., Hawksbee, D. J., & Nicolai, R. (1994) Quality measures for differential GPS positioning, The Hydrographic Journal, 72, 17–22.Google Scholar
Förstner, W. (1983) Reliability and discernability of extended Gaus-Markov models, Deutsche Geodätische Kommission (DGK), Report A, No. 98, 79–103.Google Scholar
Friedland, B. (1969) Treatment of bias in recursive filtering, IEEE Trans. Automat. Contr., AC-14(4): 359–367.CrossRefGoogle Scholar
Gillesen, I. & Elema, I. A. (1996) Test Results of DIA: A Real-Time Adaptive Integrity Monitoring Procedure, Used in an Integrated Navigation System, International Hydrographic Review, Monaco.Google Scholar
Hewitson, S., Lee, H. K. & Wang, J. (2004) Localizability Analysis for GPS/Galileo Receiver Autonomous Integrity Monitoring, The Journal of Navigation, 57, 245–259.Google Scholar
Hewitson, S. & Wang, J. (2005) GPS/GLONASS/Galileo Receiver Autonomous Integrity Monitoring (RAIM) Performance Analysis, GPS Solutions, Feb 2006, 1–16.Google Scholar
Ignagni, M. B. (1981) An alternate derivation and extension of Friedland's two-stage Kalman estimator. IEEE Trans. Aut. Contr., AC-26(3), 746–750.CrossRefGoogle Scholar
Leahy, F. & Judd, M. (1996) A simple process for smoothing route mapping by GPS, Proceedings of the 37th Australian Surveyors Conference, 13–19 April, 1996, Perth, Australia, pp. 411–421.Google Scholar
Lu, G. (1991) Quality Control for Differential Kinematic GPS Positioning, M.Sc Thesis, Department of Geomatics, University of Calgary.Google Scholar
Nikitorov, I. (2002) Integrity Monitoring for Multi-Sensor Systems. ION GPS 2002, 24–27 September, Portland OR.Google Scholar
Salzmann, M. A. (1993) Least Squares Statistical Filtering and Testing for Geodetic Navigation Systems, Netherlands Geodetic Commission, Publications on Geodesy, New Series, Vol. 37.CrossRefGoogle Scholar
Salzmann, M. A. (1995) Real-time adaptation for model errors in dynamic systems, Bulletin Géodésique, 69, 81–91.CrossRefGoogle Scholar
Sorenson, H. W. (1970) Least Squares Estimation: from Gauss to Kalman, IEEE Spectrum, No. 7, 63–68.CrossRefGoogle Scholar
Teunissen, P. J. G., (1990) Quality control in integrated navigation systems, Proc IEEE PLANS'90, Las Vegas, U.S.A.Google Scholar
Teunissen, P. J. G., (1998) Quality Control and GPS, in Teunissen, P. J. G. and Kleusberg, A. (Eds): GPS for Geodesy, Springer Verlag, 2nd Edition, 271–318.CrossRefGoogle Scholar
Tiberius, C. C. J. M. (1998a) Quality Control in Positioning, Hydrographic Journal, October, 90, 3–8.Google Scholar
Tiberius, C. C. J. M. (1998b) Recursive data processing for kinematic GPS Surveying, PhD Thesis, Delft University of Technology.Google Scholar
Wang, J., & Chen, Y. (1994) On the Localizability of Blunders in Correlated Coordinates of Junction Points in Densification Networks, Aust. J. Geod. Photogram. Surv., 60, 109–119.Google Scholar
Wang, J., Stewart, M. P. & Tsakiri, M. (1997) On quality control in hydrographic GPS surveying, Proc. 3th Australasian Hydrographic Symposium, Fremantle, Australia, Nov. 1–3, 136–141.Google Scholar