Raised cosine filter-based empirical mode decomposition

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Raised cosine filter-based empirical mode decomposition

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The empirical mode decomposition (EMD) is a relatively new method to decompose multicomponent signals that requires no a priori knowledge about the components. In this study, a modified algorithm using raised cosine interpolation is proposed which the authors refer to as raised cosine empirical mode decomposition. The decomposition quality of this proposed technique is controllable via an adjustable parameter. This results in better performance than the original approach which produces faster convergence or lower final error under different conditions. An efficient fast Fourier transform-based implementation of the proposed technique is presented. The signal decomposition performance of the new algorithm is demonstrated by application to a variety of multicomponent signals and a comparison with EMD algorithm is presented. Computational complexity of the two techniques is compared.

Inspec keywords: filtering theory; signal processing; computational complexity; fast Fourier transforms; interpolation

Other keywords: fast Fourier transform; raised cosine filter; raised cosine interpolation; empirical mode decomposition; multicomponent signal decompostion; computational complexity

Subjects: Interpolation and function approximation (numerical analysis); Integral transforms in numerical analysis; Signal processing theory; Integral transforms in numerical analysis; Interpolation and function approximation (numerical analysis); Filtering methods in signal processing

References

    1. 1)
    2. 2)
      • Stevenson, N., Mesbah, M., Boashash, B.: `A sampling limit for the empirical mode decomposition', Proc. Eighth Int. Symp. Signal Processing and its Applications (ISSPA), 28–31 August 2005, Sydney, Australia, p. 647–650.
    3. 3)
    4. 4)
    5. 5)
    6. 6)
    7. 7)
    8. 8)
      • Rilling, G., Flandrin, P., Gonçalvés, P.: `On empirical mode decomposition and its algorithms', Proc. IEEE-EURASIP Workshop on Nonlinear Signal Image Processing (NSIP), 8–11 June 2003, Grado, Italy.
    9. 9)
      • N.E. Huang , S.S.P. Shen . (2005) Hilbert–Huang transform and its applications.
    10. 10)
      • Rilling, G., Flandrin, P.: `On the influence of sampling on the empirical mode decomposition', Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing (ICASSP), 15–19 May 2006, Toulouse, France.
    11. 11)
    12. 12)
    13. 13)
      • C. Fröberg . (1969) Introduction to numerical analysis.
    14. 14)
      • Deering, R., Kaiser, J.F.: `The use of a masking signal to improve empirical mode decomposition', Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing (ICASSP), 18–23 March 2005, Philadelphia, PA.
    15. 15)
      • J.G. Proakis . (1995) Digital communications.
    16. 16)
    17. 17)
    18. 18)
    19. 19)
      • Z. Wu , N.E. Huang . A study of the characteristics of white noise using the empirical mode decomposition method. Proc. R. Soc. Lond. A , 2046 , 1597 - 1611
    20. 20)
      • ‘IOC Sea Level Monitoring Facility’, 2010. Available at: http://www.ioc-sealevelmonitoring.org.
    21. 21)
    22. 22)
    23. 23)
    24. 24)
      • Roy, A., Doherty, J.F.: `Empirical mode decomposition frequency resolution improvement using the pre-emphasis and de-emphasis method', Proc. 41st Annual Conf. on Information Sciences and Systems (CISS), 9–21 March 2008, Princeton, NJ.
    25. 25)
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