Publisher : The Korean Society for Aeronautical & Space Sciences
DOI : 10.5139/IJASS.2011.12.4.371
Title & Authors
Improvement of a Low Cost MEMS Inertial-GPS Integrated System Using Wavelet Denoising Techniques Kang, Chang-Ho; Kim, Sun-Young; Park, Chan-Gook;
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.
Low cost micro electromechanical system inertial/global positioning system integrated system;Wavelet denoising;Micro electromechanical system inertial sensor;
A New Technique for Integrating MEMS-Based Low-Cost IMU and GPS in Vehicular Navigation, Journal of Sensors, 2016, 2016, 1
2D Human Gesture Tracking and Recognition by the Fusion of MEMS Inertial and Vision Sensors, IEEE Sensors Journal, 2014, 14, 4, 1160
Robust wavelet-based inertial sensor error mitigation for tightly coupled GPS/BDS/INS integration during signal outages, Survey Review, 2016, 1
An improved noise reduction algorithm based on wavelet transformation for MEMS gyroscope, Frontiers of Optoelectronics, 2015, 8, 4, 413
Antoniadis, A. (2007). Wavelet methods in statistics: some recent developments and their applications. Statistic Surveys, 1, 16-55.
Chan, A. K. and Peng, C. (2003). Wavelets for Sensing Technologies. Boston: Artech House.
Daubechies, I. (1992). Ten Lectures on Wavelets. Philadelphia: Society for Industrial and Applied Mathematics.
Donoho, D. L. (1995). De-noising by soft-thresholding. IEEE Transactions on Information Theory, 41, 613-627.
Donoho, D. L. and Johnstone, J. M. (1994). Ideal spatial adaptation by wavelet shrinkage. Biometrika, 81, 425-455.
Donoho, D. L. and Johnstone, I. M. (1995). Adapting to unknown smoothness via wavelet shrinkage, Journal of the American Statistical Association, 90, 1200-1224.
Gao, H. (1998). Wavelet shrinkage denoising using the nonnegative garrote. Journal of Computational and Graphical Statistics, 7, 469-488.
Goswami, J. C. and Chan, A. K. (1999). Fundamentals of Wavelets: Theory, Algorithms, and Applications. New York: Wiley.
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.
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.
Nassar, S. and El-Sheimy, N. (2005). Wavelet analysis for improving INS and INS/DGPS navigation accuracy. Journal of Navigation, 58, 119-134.
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.
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.
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.