A De-Noising Method for GPR Signal Based on EEMD

Lu Gan; Long Zhou; Shan Mei Liu

doi:10.4028/www.scientific.net/AMM.687-691.3909

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 687-691A De-Noising Method for GPR Signal Based on EEMD

A De-Noising Method for GPR Signal Based on EEMD

Abstract:

Aiming at the de-noising of GPR echo signal, a de-noising method based on EEMD and wavelet is presented. First the echo signal data is processed with EEMD and yields IMF components. Then the IMF components which indicate noise are subtracted. Next, the high frequency IMF components of the remaining are subjected to wavelet threshold. Finally, the signal is reconstructed using the de-noising IMF and low frequency IMF to realize signal de-noising. Compared with other commonly used methods, EEMD-wavelet method has improvement on SNR. The experiment results show its effectiveness and feasibility in GPR de-noising.

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Periodical:

Applied Mechanics and Materials (Volumes 687-691)

Pages:

3909-3913

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.687-691.3909

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Online since:

November 2014

Authors:

Lu Gan, Long Zhou, Shan Mei Liu*

Keywords:

De-Noising, EEMD, EMD, GPR

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References

[1] Hon, T.K. ; Subramaniam, and S.R. ; Georgakis, A. STFT-based denoising of biomechanical impact signals, Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE, p.4036–4039, (2010).

DOI: 10.1109/iembs.2010.5628013

Google Scholar

[2] Ogino, Y. ; Uchikado, T. ; Nakano, K. ; Nakamura, Y. ; Ogawa, T. ; Matsuyama, T., Denoising and detection of reflected waves from buried pipes with ground-penetrating radar data, Synthetic Aperture Radar (APSAR), pp.129-132, (2013).

Google Scholar

[3] Xiao-Li Chen ; Mao Tian ; Wen-Bing Yao, GPR signals de-noising by using wavelet networks, Machine Learning and Cybernetics, Volume: 8, pp: 4690-4693, (2005).

DOI: 10.1109/icmlc.2005.1527766

Google Scholar

[4] Gavrovska, A. ; Slavkovic, M. ; Reljin, I. ; Reljin, B., Application of wavelet and EMD-based denoising to phonocardiograms, Signals, Circuits and Systems (ISSCS), pp: 1-4, (2013).

DOI: 10.1109/isscs.2013.6651264

Google Scholar

[5] WU Zhaohua, HUANG N E. Ensemble empirical mode decomposition: a noise-assisted data analysis method[J]. World Scientific, 2009, 1(1): 1-41.

Google Scholar

[6] HUANG N E, SHEN Z, STEVEN R L, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proc. R. Soc. Lond. A, 1998, 454(1971): 903-995.

DOI: 10.1098/rspa.1998.0193

Google Scholar

[7] Flandrin P, Rilling G, Goncalves G．Empirical mode decomposition as a filter bank[J]．1EEE Signal Processing Letters, 2004, 11(2): 112-l14.

DOI: 10.1109/lsp.2003.821662

Google Scholar