DOI QR코드

DOI QR Code

Improvement of a Low Cost MEMS Inertial-GPS Integrated System Using Wavelet Denoising Techniques

  • Kang, Chang-Ho (School of Mechanical and Aerospace Engineering/Automation and Systems Research Institute/The Institute of Advanced Aerospace Technology & Automation and Systems Research Institute, Seoul National University) ;
  • Kim, Sun-Young (School of Mechanical and Aerospace Engineering/Automation and Systems Research Institute/The Institute of Advanced Aerospace Technology & Automation and Systems Research Institute, Seoul National University) ;
  • Park, Chan-Gook (School of Mechanical and Aerospace Engineering/Automation and Systems Research Institute/The Institute of Advanced Aerospace Technology & Automation and Systems Research Institute, Seoul National University)
  • Received : 2011.11.03
  • Accepted : 2011.12.07
  • Published : 2011.12.30

Abstract

In this paper, the wavelet denoising techniques using thresholding method are applied to the low cost micro electromechanical system (MEMS)-global positioning system(GPS) integrated system. This was done to improve the navigation performance. The low cost MEMS signals can be distorted with conventional pre-filtering method such as low-pass filtering method. However, wavelet denoising techniques using thresholding method do not distort the rapidly-changing signals. They can reduce the signal noise. This paper verified the improvement of the navigation performance compared to the conventional pre-filtering by simulation and experiment.

Keywords

References

  1. Antoniadis, A. (2007). Wavelet methods in statistics: some recent developments and their applications. Statistic Surveys, 1, 16-55. https://doi.org/10.1214/07-SS014
  2. Chan, A. K. and Peng, C. (2003). Wavelets for Sensing Technologies. Boston: Artech House.
  3. Daubechies, I. (1992). Ten Lectures on Wavelets. Philadelphia: Society for Industrial and Applied Mathematics.
  4. Donoho, D. L. (1995). De-noising by soft-thresholding. IEEE Transactions on Information Theory, 41, 613-627. https://doi.org/10.1109/18.382009
  5. Donoho, D. L. and Johnstone, J. M. (1994). Ideal spatial adaptation by wavelet shrinkage. Biometrika, 81, 425-455. https://doi.org/10.1093/biomet/81.3.425
  6. Donoho, D. L. and Johnstone, I. M. (1995). Adapting to unknown smoothness via wavelet shrinkage, Journal of the American Statistical Association, 90, 1200-1224. https://doi.org/10.2307/2291512
  7. Gao, H. (1998). Wavelet shrinkage denoising using the nonnegative garrote. Journal of Computational and Graphical Statistics, 7, 469-488. https://doi.org/10.2307/1390677
  8. Goswami, J. C. and Chan, A. K. (1999). Fundamentals of Wavelets: Theory, Algorithms, and Applications. New York: Wiley.
  9. Hasan, A. M., Samsudin, K., Ramli, A. R., and Azmir, R. S. (2010). Comparative study on wavelet filter and thresholding selection for GPS/INS data fusion. International Journal of Wavelets, Multiresolution and Information Processing, 8, 457-473. https://doi.org/10.1142/S0219691310003572
  10. Kang, C. W. and Park, C. G. (2009). Improvement of INSGPS integrated navigation system using wavelet thresholding. Journal of the Korean Society for Aeronautical and Space Sciences, 37, 767-773. https://doi.org/10.5139/JKSAS.2009.37.8.767
  11. Nassar, S. and El-Sheimy, N. (2005). Wavelet analysis for improving INS and INS/DGPS navigation accuracy. Journal of Navigation, 58, 119-134. https://doi.org/10.1017/S0373463304003005
  12. 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, 15, 404-412. https://doi.org/10.1088/0957-0233/15/2/013
  13. Titterton, D. H., Weston, J. L., and Institution of Electrical Engineers (1997). Strapdown Inertial Navigation Technology. London, UK: Peter Peregrinis Ltd. on behalf of the Institution of Electrical Engineers.
  14. Yoon, B. J. and Vaidyanathan, P. P. (2004). Wavelet-based denoising by customized thresholding. Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, Montreal, Canada. pp. II925-II928.

Cited by

  1. A New Technique for Integrating MEMS-Based Low-Cost IMU and GPS in Vehicular Navigation vol.2016, 2016, https://doi.org/10.1155/2016/5365983
  2. A new MEMS gyroscope drift suppression method for the low-cost untwisting spin platform vol.12, pp.2, 2017, https://doi.org/10.1002/tee.22373
  3. Collision Vehicle Detection System Based on GPS/INS Integration vol.05, pp.02, 2017, https://doi.org/10.4236/jcc.2017.52006
  4. Robust wavelet-based inertial sensor error mitigation for tightly coupled GPS/BDS/INS integration during signal outages vol.49, pp.357, 2017, https://doi.org/10.1080/00396265.2016.1190162
  5. An improved noise reduction algorithm based on wavelet transformation for MEMS gyroscope vol.8, pp.4, 2015, https://doi.org/10.1007/s12200-015-0474-2
  6. New GPS-Aided SINU System Modeling using an Autoregressive Model vol.12, pp.9, 2015, https://doi.org/10.5772/61201
  7. 2D Human Gesture Tracking and Recognition by the Fusion of MEMS Inertial and Vision Sensors vol.14, pp.4, 2014, https://doi.org/10.1109/JSEN.2013.2288094