Journal of Information Organization


Vol No. 13 ,Issue No. 2 2023

Low-noise MEMS Sensors for GPS data
Lachezar Hristov, Emil Iontchev, Rosen Miletiev and Petar Kapanakov
Lachezar Hristov is with the Faculty of Telecommunications and Electrical Equipment in Transport “Todor Kableshkov” University of Transport 158 Geo Milev Str, Sofia 1574, Bulgaria., Rosen Miletiev is with the Faculty of Telecommunications at Technical
Abstract: To get perfect navigational data for positions, speed, acceleration and direction we applied a wavelet-based algorithm. This algorithm is used to reduce noise obtained from MEMS-based sensors. The treatment to limit the different random errors with signals is used to get low-cost MEMS sensors. GPS data is used to experiment with the introduced algorithm.
Keywords: Wavelet Transform, Denoising, Inertial Sensors Low-noise MEMS Sensors for GPS data
DOI:https://doi.org/10.6025/jio/2023/13/2/53-61
Full_Text   PDF 1.42 MB   Download:   72  times
References:

[1] Iontchev, E. Algorithm for denoising parameters selection with wavelets, Elektrotechnica & Elektronica E+E, Vol. 47 No 3- 4/2012, pp 37.
[2] Kang, C.W. and Park, C.G. Improvement of INS-GPS Integrated Navigation System using Wavelet Thresholding, Journal of The Korean Society for Aeromautical and Space Sciences, Vol.37, No.8, 2009.
[3] Hasan, A.M. (2010). Comparative study on Wavelet Filter and Thresholding Selection For GPS/INS Data Fusion, International Journal of Wavelets, Multiresolution and Information Processing, Vol 8, No.3, 2010.
[4] Vetova, S., Draganov, I. and Ivanov, I. (2018). CBIR with Dual Tree Complex Wavelet Transform using Maximally Flat All-pass Filter”, Journal Electrotechnics and Electronics, “+”, vol. 53, 11-12, 2018, pp. 314-320, ISSN 0861-4717.
[5] Vetova, S. and Ivanov, I. (2014) . Image Features Extraction Using The Dual-Tree Complex Wavelet Transform, 2nd International Conference on Mathematical, Computational And Statistical Sciences, Gdansk, Poland, 2014, pp. 277 – 282, ISBN: 978-960- 474-380-3.
[6] Vetova, S. and Ivanov, I. (2014) . Content–Based Image Retrieval Algorithm Based On The Dual-Tree Complex Wavelet Transform: Efficiency Analysis, 5th European Conference of Computer Science, Geneva, Switzerland, 2014, pp. 71 – 77, ISBN: 978-1-61804-264-4.
[7] Goswami, J.C. and Chan, A.K. (1999). Fundamentals of Wavelets: Theory, Algorithms, and Applications, Wiley, 1999.
[8] Crouse, M.S., Nowak, R.D. and Baraniuk, R.G. (1998). Waveletbased signal processing using hidden Markov models, IEEE Trans. Signal Proc., vol. 46, pp. 886-902, April.
[9] Gao, H.-Y. and Bruce, A.G. (1996). WaveShrink with firm shrinkage, Technical Report 39, StatSci Division of MathSoft, Inc. 1996.