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A Frequency-Domain INS/GPS Dynamic Response Method for Bridging GPS Outages

Published online by Cambridge University Press:  13 September 2010

Mohammed El-Diasty  and
Spiros Pagiatakis
Mohammed El-Diasty*
Affiliation:
(Dept. of Earth and Space Science and Engineering York University, Toronto)
Spiros Pagiatakis
Affiliation:
(Dept. of Earth and Space Science and Engineering York University, Toronto)
*
(Email: eldiasty@yorku.ca)
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Abstract

We develop a new frequency-domain dynamic response method to model integrated Inertial Navigation System (INS) and Global Positioning System (GPS) architectures and provide an accurate impulse-response-based INS-only navigation solution when GPS signals are denied (GPS outages). The input to such a dynamic system is the INS-only solution and the output is the INS/GPS integration solution; both are used to derive the transfer function of the dynamic system using Least Squares Frequency Transform (LSFT). The discrete Inverse Least Squares Frequency Transform (ILSFT) of the transfer function is applied to estimate the impulse response of the INS/GPS system in the time domain. It is shown that the long-term motion dynamics of a DQI-100 IMU/Trimble BD950 integrated system are recovered by 72%, 42%, 75%, and 40% for north and east velocities, and north and east positions respectively, when compared with the INS-only solution (prediction mode of the INS/GPS filter). A comparison between our impulse response model and the current state-of-the-art time-domain feed-forward neural network shows that the proposed frequency-dependent INS/GPS response model is superior to the neural network model by about 26% for 2D velocities and positions during GPS outages.

Keywords

INS/GPS integrationimpulse responsedynamic systemGPS outages
Type
Research Article
Information
The Journal of Navigation , Volume 63 , Issue 4 , October 2010 , pp. 627 - 643
Copyright
Copyright © The Royal Institute of Navigation 2010

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References

REFERENCES

Abd-Elhamid, W., Noureldin, A. and El-Sheimy, N. (2007). Adaptive Fuzzy Modeling of Low Cost Inertial Based Positioning Errors. IEEE Transactions on Fuzzy Systems, 15 (3), 519–529.CrossRefGoogle Scholar
Azimi-Sadjadi, B. (2001). Approximate Nonlinear Filtering with Applications to Navigation. PhD Thesis, University of Maryland at College Park.Google Scholar
BEI Technologies, Inc. (2002). DQI-100 Digital Quartz IMU: User's Guide. Concord, California 94518-1399, USA.Google Scholar
Buttkus, B. (2000). Spectral Analysis and Filter Theory in Applied Geophysics. Springer-Verlag, Germany.CrossRefGoogle Scholar
Craymer, M.R. (1998). The least squares spectrum, its inverse transform and autocorrelation function: theory and some applications in geodesy. PhD thesis, University of Toronto, Canada.Google Scholar
Chiang, K. W., Noureldin, A. and El-Sheimy, N. (2003). Multi-sensors Integration using Neuron Computing for Land Vehicle Navigation. GPS Solutions., 6, 3, 209–218.CrossRefGoogle Scholar
Chiang, K., Noureldin, A. and El-Sheimy, N. (2006). Examining the use of stored navigation knowledge for neural network based INS/GPS integration. Geomatica, 60, 47–57.Google Scholar
Chiang, K-W, Noureldin, A., and El-Sheimy, N. (2008). Developing a Low Cost MEMS IMU/GPS Integration Scheme Using Constructive Neural Networks. IEEE Transactions on Aerospace and Electronic Systems, 44, 2, 582–594.CrossRefGoogle Scholar
Haykin, S. (1999). Neural Networks: A Comprehensive Foundation. Second Edition. Prentice Hall.Google Scholar
Julier, S. J. and Uhlmann, J. K. (1997). A New Extension of the Kalman Filter to Nonlinear Systems. Proc. SPIE – Int. Soc. Opt. Eng. (USA), vol. 3068, (Orlando, FL), 182–193.CrossRefGoogle Scholar
Jwo, D.-J. and Huang, H.-C., (2004). Neural network aided adaptive extended Kalman filtering approach for DGPS positioning. Journal of Navigation, 57, 449–463.CrossRefGoogle Scholar
Kaygisiz, B., Erkmen, A. and Erkmen, I. (2004). GPS/INS Enhancement for Land Navigation using Neural Network. Journal of Navigation, 57, 297–310.CrossRefGoogle Scholar
Kaygısız, B., Erkmen, A. and Erkmen, I. (2007). Enhancing positioning accuracy of GPS/INS system during GPS outages utilizing artificial neural network. Neural Processing Letters, 25, 171–186.CrossRefGoogle Scholar
Kong, X. (2000). Improving Inertial Navigation System Algorithms for Low Cost IMU. PhD Thesis, Department of Mechanical and Mechatronic Engineering, The University of Sydney, Australia.Google Scholar
Mikhail, EM. (1976). Observations and Least Squares. Thomas Y. Crowell Company, New York, 1976.Google Scholar
Nassar, S. and Schwarz, K. P. (2002). Bridging DGPS Outages in Kinematic Applications Using a Simple Algorithm for INS Bias Modeling. Proc. ION GPS-2002, Portland, USA, Sept. 24–27, 2002, pp. 1474–1482.Google Scholar
Nordlund, P-J, and Gustafsson, F. (2001). Sequential Monte Carlo filtering techniques applied to integrated navigation systems. Proceedings of the American Control Conference, VA, June 25–27.CrossRefGoogle Scholar
Nørgaard, M., Poulsen, N. K. and Ravn, O. (2000a). A dvances in derivative free state estimation to nonlinear systems. Technical Report, IMM-REP-1998-15, Department of Mathematical Modeling, DTU.Google Scholar
Nørgaard, M., Ravn, O.Poulsen, N. K., Hansen, L. K. (2000b). Neural networks for Modelling and Control of Dynamic Systems. Springer-Verlag, London, UK.CrossRefGoogle Scholar
Noureldin, A., Osman, A. and El-Sheimy, N. (2004). A Neuro-Wavelet Method for Multi-Sensor System Integration for Vehicular Navigation. Measurement Science and Technology, IoP, 15(2), 404–412.CrossRefGoogle Scholar
Pagiatakis, SD. (1999). Stochastic significance of peaks in the least-squares spectrum. Journal of Geodesy 73, 67–78.CrossRefGoogle Scholar
Pintelon, R. and Schoukens, J. (2001). System Identification – A Frequency Domain Approach. IEEE Press, Piscataway.CrossRefGoogle Scholar
Semeniuk, L. and Noureldin, A. (2006). Bridging GPS outages using neural network estimates of INS position and velocity errors. Measurement Science and Technology, IoP, 17 (9), 2782–2798.Google Scholar
Shin, E-H. (2005). Estimation Techniques for Low-Cost Inertial Navigation. PhD. thesis, Department of Geomatics Engineering, University of Calgary, Calgary, Alberta, Canada, UCGE Report No. 20219.Google Scholar
van der Merwe, R., Wan, E. A., and Julier, S. (2004). Sigma-Point Kalman Filters for Nonlinear Estimation and Sensor-Fusion: Applications to Integrated Navigation. Proceedings of the AIAA Guidance, Navigation & Control Conference (GNC), Providence, Rhode Island.CrossRefGoogle Scholar
Vanícek, P. (1969). Approximate Spectral Analysis by Least-squares Fit. Astrophysics and Space Science 4, 387–391.CrossRefGoogle Scholar
Vanícek, P, Krakiwsky, E. (1986). Geodesy, the concepts. North Holland, Amsterdam (2nd Edition).Google Scholar
Wang, J. J., Wang, J., Sinclair, D. and Watts, L., (2006). Designing a Neural Network for GPS/INS/PL Integration. IGNSS-2006, Menay Pty Ltd, Gold Coast Australia.Google Scholar