Skip to main content
SpringerLink
Log in

Intelligently tuned wavelet parameters for GPS/INS error estimation

  • Published:
International Journal of Automation and Computing Aims and scope Submit manuscript

Abstract

This paper presents a new algorithm for de-noising global positioning system (GPS) and inertial navigation system (INS) data and estimates the INS error using wavelet multi-resolution analysis algorithm (WMRA)-based genetic algorithm (GA) with a well-designed structure appropriate for practical and real time implementations because of its very short training time and elevated accuracy. Different techniques have been implemented to de-noise and estimate the INS and GPS errors. Wavelet de-noising is one of the most exploited techniques that have been recently used to increase the precision and reliability of the integrated GPS/INS navigation system. To ameliorate the WMRA algorithm, GA was exploited to optimize the wavelet parameters so as to determine the best wavelet filter, thresholding selection rule (TSR), and the optimum level of decomposition (LOD). This results in increasing the robustness of the WMRA algorithm to estimate the INS error. The proposed intelligent technique has overcome the drawbacks of the tedious selection for WMRA algorithm parameters. Finally, the proposed method improved the stability and reliability of the estimated INS error using real field test data.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. M. S. Grewal, L. R. Weill, A. P. Andrews. Global Positioning Systems, Inertial Navigation and Integration, 2nd ed., USA: John Wiley & Sons, Interscience, 2007.

    Book  Google Scholar 

  2. A. Leick. GPS Satellite Surveying, USA: Wiley Interscience, 1992.

    Google Scholar 

  3. D. H. Titterton, J. L. Weston. Strapdown Inertial Navigation Technology, 2nd ed., USA: AIAA, 2004.

    Book  Google Scholar 

  4. F. A. Faruqi, K. J. Turner. Extended Kalman filter synthesis for integrated global positioning/inertial navigation systems. Applied Mathematics and Computation, vol. 115, no. 2–3, pp. 213–227, 2000.

    Article  MATH  MathSciNet  Google Scholar 

  5. N. E. Sheimy, A. Osman, A. Noureldin, S. Nassar. A new way to integrate GPS and INS: Wavelet multi-resolution analysis. GPS World, pp. 70–78, 2003.

  6. K. W. Chiang, Y. W. Huang. An intelligent navigator for seamless INS/GPS integrated land vehicle navigation application. Applied Soft Computing, vol. 8, no. 1, pp. 722–733, 2008.

    Article  MathSciNet  Google Scholar 

  7. S. Nassar. Improving the Inertial Navigation System (INS) Error Model for INS and INS/DGPS Application, Ph.D. dissertation, Department of Geomatics Engineering, University of Galgary, Canada, 2003.

    Google Scholar 

  8. A. M. Hasan, K. Samsudin, A. R. Ramli, R. S. Azmir. Analysis of wavelet threshold de-noising for GPS/INS system. In Proceedings of IEEE Student Conference on Research and Development, Johor, Malaysia, pp. 751–754, 2008.

  9. A. M. Hasan, K. Samsudin, A. R. Ramli, R. S. Azmir. Comparative study on wavelet filter and thresholding selection for GPS/INS data fusion. International Journal ofWavelets, Multiresolution and Information Processing, vol. 8, no. 3, pp. 457–473, 2010.

    Article  MATH  Google Scholar 

  10. A. Noureldin, A. Osman, N. E. Sheimy. A neuro-wavelet method for multi-sensor system integration for vehicular navigation. Measurement Science and Technology, vol. 15, no. 2, pp. 404–412, 2004.

    Article  Google Scholar 

  11. J. Farrell, M. Barth. The Global Positioning System and Inertial Navigation, USA: McGraw-Hill Companies, 1999.

    Google Scholar 

  12. M. Park. Error analysis and stochastic modeling of MEMS based inertial sensors for land vehicle navigation applications, M. Sc. dissertation, University of Calgary, Canada, 2004.

    Google Scholar 

  13. R. Sharaf, A. Noureldin. Sensor integration for satellitebased vehicular navigation using neural networks. IEEE Transactions on Neural Networks, vol. 18, no. 2, pp. 589–594, 2007.

    Article  Google Scholar 

  14. M. S. Grewal, L. R. Weill, A. P. Andrews. Global Positioning Systems Inertia Navigation and Integration, USA: John Wiley and Sons, Interscience, 2007.

    Book  Google Scholar 

  15. D. H. Titterton, J. L. Weston. Strapdown Inertial Navigation Technology, The Institution of Electrical Engineers, 2004.

  16. Z. Syed, P. Aggarwal, Y. Yang, N. E. Sheimy. Improved vehicle navigation using aiding with tightly coupled integration. In Proceedings of the 68th Vehicular Technology Conference, Calgary, Canada, pp. 3077–3081, 2008.

  17. W. Wang, Z. Liu, R. Xie. Quadratic extended Kalman filter approach for GPS/INS integration. Aerospace Science and Technology, vol. 10, no. 8, pp. 709–713, 2006.

    Article  MATH  Google Scholar 

  18. B. A. Sadjadi, P. S. Krishnaprasad. Approximate nonlinear filtering and its application in navigation. Automatica, vol. 41, no. 6, pp. 945–956, 2005.

    Article  MATH  MathSciNet  Google Scholar 

  19. N. Aboelmagd, E. S. Ahmed, M. R. Taha. Optimizing neuro-fuzzy modules for data fusion of vehicular navigation systems using temporal cross-validation. Engineering Applications of Artificial Intelligence, vol. 20, no. 1, pp. 49–61, 2007.

    Article  Google Scholar 

  20. A. Noureldin, R. Sharaf, A. Osman, N. E. Sheimy. INS/GPS data fusion technique utilizing radial basis functions neural networks. In Proceedings of Position Location and Navigation Symposium, IEEE, Monterey, USA, pp. 280–284, 2004.

    Google Scholar 

  21. K. W. Chiang. INS/GPS Integration Using Neural Networks for Land Vehicular Navigation Applications, Ph. D. dissertation, University of Calgary, Canada, 2004.

    Google Scholar 

  22. S. S. S. Sindhu, S. Geethaa, M. Marikannan, A. Kannan. A neuro-genetic based short-term forecasting framework for network intrusion prediction system. International Journal of Automation and Computing, vol. 6, no. 4, pp. 406–414, 2009.

    Article  Google Scholar 

  23. T. Liu, C. Chen, Z. Li, J. Chou. Method of inequalitiesbased multiobjective genetic algoithm for optimizing a chart-double-pendulum system. International Journal of Automation and Computing, vol. 6, no. 1, pp. 29–37, 2009.

    Article  Google Scholar 

  24. A. M. Hasan, K. Samsudin, A. R. Ramli, R. S. Azmir. Automatic estimation of inertial navigation system errors for global positioning system outage recovery. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, vol. 225, no. 1, pp. 86–96, 2011.

    Article  Google Scholar 

  25. R. S. Asamwar, K. M. Bhurchandi, A. S. Gandhi. Interpolation of images using discrete wavelet transform to simulate image resizing as in human vision. International Journal of Automation and Computing, vol. 7, no. 1, pp. 9–16, 2010.

    Article  Google Scholar 

  26. C. S. Burrus, R. A. Gopenath, H. Guo. Introduction to Wavelet and Wavelet Transform, USA: Prentice Hall, 1998.

    Google Scholar 

  27. S. Prabakaran, R. Sahu, S. Verma. A clustering and selection method using wavelet power spectrum. International Journal of Computer Science, vol. 32, no. 4, pp. 1–5, 2006.

    Google Scholar 

  28. M. Misiti, Y. Misiti, G. Oppenheim, J. M. Poggi. Wavelet Toolbox: User_s Guide, Version 2, Mathworks, 2002.

  29. V. Prasad, P. Siddaiah, P. Rao. A new wavelet based method for denoising of biological signals. International Journal of Computer Science and Network Security, vol.8, no. 1, pp. 238–244, 2008.

    Google Scholar 

  30. D. L. Donoho. De-noising by soft thresholding. IEEE Transactions on Information Theory, vol. 41, no. 3, pp. 613–627, 1995.

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Systems Research Group, Department of Computer and Communication Systems, University Putra Malaysia, 43400, UPM-Serdang, Malaysia

    Ahmed Mudheher Hasan, Khairulmizam Samsudin & Abd Rahman Ramli

Authors
  1. Ahmed Mudheher Hasan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Khairulmizam Samsudin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Abd Rahman Ramli
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ahmed Mudheher Hasan.

Additional information

This work was supported in part by Graduate School of Studies through the Graduate Research Fellowship (GRF) sponsored by University Putra Malaysia.

Ahmed Mudheher Hasan received the B. Sc. degree in control engineering from the Control and Systems Engineering Department, University of Technology in 2002, the M. S. degree in computer control from the same university in 2005, and now a Ph.D candidate in computer and communication engineering in University Putra Malaysia. He worked as a tutor and assistant lecturer in the University of Technology from 2003 to 2007, and he is now a research assistant at the University Putra Malaysia from 2008.

His research interests include intelligent control systems, evolutionary algorithms, navigation systems, and signal processing.

Khairulmizam Samsudin received the Ph.D. degree in electrical and electronics engineering from the University of Glasgow in 2006. He specializes in computer architecture focusing on interfacing transistor-level and architectural-level modeling for future computer systems. He currently leads the Computer Systems Research Group at the Department of Computer and Communication Systems Engineering, University Putra Malaysia.

His research interests include computer architecture, operating system, embedded system, swarm mobile-robot and biologically inspired computing architecture.

Abd Rahman Ramli received the B. Sc. degree in electronics at the National University of Malaysia in 1982. Then he obtained his master’s degree in information technology system at the University of Strathclyde, UK in 1985. In 1990, he pursued his doctoral studies at the University of Bradford, UK. He was appointed as the Head of Computer and Communication System Engineering in August 1996 until July 1998. Currently, he serves as head of Intelligent Systems and Robotics Laboratory, Institute of Advanced Technology, University Putra Malaysia where he leads a cutting edge research laboratory in real-time and embedded systems, intelligent systems and perceptual robotics. He is also appointed as head of Multimedia System Engineering Research Group, Department of Computer and Communication Systems Engineering, Faculty of Engineering.

His research interests include image processing and electronic imaging, multimedia system engineering, Internet computing, smart card applications, embedded system, computer remote monitoring system and intelligence systems.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Hasan, A.M., Samsudin, K. & Ramli, A.R. Intelligently tuned wavelet parameters for GPS/INS error estimation. Int. J. Autom. Comput. 8, 411–420 (2011). https://doi.org/10.1007/s11633-011-0598-9

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11633-011-0598-9

Keywords

Search

Navigation