Abstract
Precise position and navigation with GPS is always required for both civil and military applications. The errors and biases associated with navigation will change the positional information from centimeters to several meters. To estimate and mitigate the errors in GPS positioning data, the wavelet transform is most significant technique and proven. The traditional wavelet threshold methods will work to a certain extent but are not useful to estimate the signal levels to the expected level due to their incapability for capturing the joint statistics of the wavelet coefficients. The wavelet-based hidden Markov tree (WHMT) is designed to capture such dependencies by modeling the statistical properties of the wavelet coefficients as well. In this paper, a WHMT is proposed to reduce positioning error of the GPS data. To establish proposed method, the position data are decomposed using wavelets. The obtained wavelet coefficients are subjected to Discrete Wavelet Transform (DWT) as well-proposed WHMT for noise removal. In this proposed methodology, an Expectation Maximization (EM) algorithm used for computing the model parameters. The root-mean square error (RMSE) of proposed method shows better performance comparatively classical DWT.
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References
Misra, P., Enge, P.: Global Positioning system(GPS), signals, measurements and performance. Ganga-Jamun, Press, P.O. Box-633, Lincoln, MA 01773. ISBN:0-9709544-1-7
Donoho, D.L., Johnstone, I.M.: Ideal spatial adaptation via wavelet shrinkage. Biometrika 81, 425–455 (1994)
Crouse, M.S., Nowak, R.D., Baraniuk, R.G.: Wavelet-based statistical signal processing using hidden markov modeles. IEEE Trans. Signal Proc. 46, 886–902 (1998)
Romberg, J.K., Choi, H., Baraniuk, R.G.: Bayesian tree –structured image modeling using wavelet—domain hidden markov models. In: Proceedings of SPIE, vol. 3816, pp. 31–44. Denvor, CO, July 1999
Collin, F., Warrant, R.: Applications of the wavelet transform for GPS cycle slip correction and comparison with kalamanfilter. Manuscript Geodatic (1995)
Fu, W.X., Rizos, C.: The applications of wavelets to GPS signal processings. In: 10th International Technical Meeting of the Satellite Division of the U.S Insititute of Navigation, pp. 1385–1388. Kansas City, Missouri, 16–19 Sept. 1997
Xia, L.: Approach for multipath reduction using wavelet algorithm. In: Proceedings of the International Technical Meeting of the Satellite Division of the Institute of Naviagation, vol. 1, pp. 2134–2143. Kansas City, 11–14 Sept. 2001
Satirpod, C., Ogeja, J., Wang, R.: An approach to GPS analysis incorporating wavelet decomposition. Artificial Satellite 36, 27–25 (2001)
Satirpod, C., Ogeja, J., Wang, R.: GPS analysis with the aids of wavelets. In: Proceedings of the International Symposium Satellite Navigation Artificial Satellite, vol. 36, pp. 27–25, 200ion Technology & Applications, Canberra, Austrelia (2001)
Mosavi, M.R., EmmamGholipour, I.: Denoising of GPS receivers positioning data using wavelet transform and ilateral filtering. Wireless Personal communications, pp. 2295–2312 (2013)
Nassar, S., El Sheimy, N.: Wavelet analysis for improving INS/DGPS navigation accuracy. J. Navig. 58(1), 119134 (2005)
Kang, C.W., Kang, C.H., Park, C.G.: Wavelet denoising technique for improvement of the low cost MEMSGPS integrated system. In: International Symposium on GPS/GNSS, p. 2628 (2010)
Huang, D., Ding, X., Chen, Y., Gong, T., Xiong, Y.: Wavelet filters based separation of gps multipath effects and engineering structure vibrations. Acta Geodaetica Et Carographic Sinica 1, 7 (2001)
Acknowledgments
The author Ch Mahesh expresses sincere thanks to S. Nandulal, Sr. Manager (CNS), GAGAN, AAI, and R. Pavan Kumar Reddy for their continuous support and providing valuable suggestions for this project.
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Mahesh, C., Ravindra, K., Prasad, V.K. (2016). Denoising of GPS Positioning Data Using Wavelet-Based Hidden Markov Tree. In: Satapathy, S., Raju, K., Mandal, J., Bhateja, V. (eds) Proceedings of the Second International Conference on Computer and Communication Technologies. Advances in Intelligent Systems and Computing, vol 379. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2517-1_59
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DOI: https://doi.org/10.1007/978-81-322-2517-1_59
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