The Modified Ensemble Empirical Mode Decomposition Method and Extraction of Oceanic Internal Wave from Synthetic Aperture Radar Image

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  • (1. Information Security Center, State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China; 2. School of Information, Beijing Wuzi University, Beijing 101149, China; 3. School of Applied Science, Beijing Information Science and Technology University, Beijing 100192, China)

Online published: 2015-04-02

Abstract

In this paper a modified ensemble empirical mode decomposition (EEMD) method is presented, which is named winning-EEMD (W-EEMD). Two aspects of the EEMD, the amplitude of added white noise and the number of intrinsic mode functions (IMFs), are discussed in this method. The signal-to-noise ratio (SNR) is used to measure the amplitude of added noise and the winning number of IMFs (which results most frequency) is used to unify the number of IMFs. By this method, the calculation speed of decomposition is improved, and the relative error between original data and sum of decompositions is reduced. In addition, the feasibility and effectiveness of this method are proved by the example of the oceanic internal solitary wave.

Cite this article

WANG Jing-tao1,3 (王静涛), XU Xiao-ge2,3* (许晓革), MENG Xiang-hua3? (孟祥花) . The Modified Ensemble Empirical Mode Decomposition Method and Extraction of Oceanic Internal Wave from Synthetic Aperture Radar Image[J]. Journal of Shanghai Jiaotong University(Science), 2015 , 20(2) : 243 -250 . DOI: 10.1007/s12204-015-1614-y

References

[1] Huang N E, Shen Z, Long S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis [J].Proceedings of the Royal Society A, 1998, 454: 903-995.
[2] Huang N E, Shen Z, Long S R. A new view of water waves-the Hilbert spectrum [J]. Annual Review of Fluid Mechanics, 1999, 31: 417-457.
[3] Wu Z H, Huang N E. A study of the characteristics of white noise using the empirical mode decomposition method [J]. Proceedings of the Royal Society,2004, 460(2046): 1597-1611.
[4] Zhang J, Yan R Q, Gao R X, et al. Performance enhancement of ensemble empirical mode decomposition[J]. Mechanical Systems and Signal Processing, 2010,24: 2104-2123.
[5] Huang J Z, Xie J, Gao Q H, et al. A signal threshold denoising method based on improved EEMD [J]. International Review on Computers and Software, 2012,7(7): 3600-3604.
[6] Colominas M A, Schlotthauer G, Torres M E, et al. Noise-assisted EMD methods in action [J].Advances in Adaptive Data Analysis, 2012, 4(4):1250025-1250035.
[7] Zheng Y, Yue J, Sun X F, et al. Studies of filtering effect on internal solitary wave flow field data in the South China Sea using EMD [J]. Advanced Materials Research, 2012, 518: 1425-1442.
[8] Song H B, Bai Y, Pinheiro L, et al. Analysis of ocean internal waves imaged by multichannel reaction seismics, using ensemble empirical mode decomposition[J]. Journal of Geophysics and Engineering, 2012,9: 302-311.
[9] Liu A K, Hsu M K. Internal waves study in the South China Sea using synthetic aperture radar [J]. International Journal of Remote Sensing, 2004, 25(78): 1261-1264.
[10] Wang J, Yang X X,Wu L X, et al. Impact of internal waves on the coherent underwater acoustic communication[J]. AIP Conference Proceedings, 2012, 1495(1):424-431.
[11] Zheng P N, Li X T, Miao J J. Internal wave real time measurement system [J]. Applied Mechanics and Materials, 2013, 405: 3041-3044.
[12] John D G, James H M, Ilker F. Revisiting internal waves and mixing in the Arctic Ocean [J]. Journal of Geophysical Research: Oceans, 2013, 118(8): 3966-3977.
[13] Gan X L, Huang W G, Yang J S, et al. Internal wave packet characterization from SAR images using empirical mode decomposition [C]// Congress on Image and Signal Processing. Sanya, China: IEEE, 2008:750-753.
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