Advertisement

Wavelet-Packet Identification of Dynamic Systems with Coloured Measurement Noise

  • Henrique Mohallem Paiva
  • Roberto Kawakami Harrop Galvão
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5099)

Abstract

This paper analyses the effect of coloured noise on a recently proposed technique for linear system identification in frequency subbands using wavelet packets. For this purpose, a simulation study involving the longitudinal dynamics of a flexible aircraft model is presented. The results reveal that the wavelet-packet identification outcome is robust with respect to changes in the spectral noise features. In particular, the identified frequency response is effectively smoothed in regions with poor signal-to-noise ratio. Finally, the results are favourably compared, in terms of resonance peak identification, with those obtained by standard time-domain identification methods.

Keywords

Leaf Node Wavelet Packet Decomposition Tree Frequency Subbands ARMAX Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Abdelghani, M., Verhaegen, M., Van Overschee, P., de Moor, B.: Comparison study of subspace identification methods applied to flexible structures. Mechanical Systems and Signal Processing 12(5), 679–692 (1998)CrossRefGoogle Scholar
  2. 2.
    Burcharles, A., Vacher, P.: Flexible aircraft model identification for control law design. Aerospace Science and Technology 6, 591–598 (2002)CrossRefGoogle Scholar
  3. 3.
    Chen, H.X., Chua Patrick, S.K., Lim, G.H.: Adaptive wavelet transform for vibration signal modelling and application in fault diagnosis of water hydraulic motor. Mechanical Systems and Signal Processing 20(8), 2022–2045 (2006)CrossRefGoogle Scholar
  4. 4.
    Coifman, R.R., Wickerhauser, M.V.: Entropy-based algorithms for best basis selection. IEEE Transactions on Information Theory 32, 712–718 (1992)Google Scholar
  5. 5.
    Cooper, J.E., Desforges, M.J., Wright, J.R.: Modal parameter identification using an unknown coloured random input. Mechanical Systems and Signal Processing 9(6), 685–695 (1995)CrossRefGoogle Scholar
  6. 6.
    Dorfan, Y., Feuer, A., Porat, B.: Modeling and identification of LPTV systems by wavelets. Signal Processing 84, 1285–1297 (2004)zbMATHCrossRefGoogle Scholar
  7. 7.
    Erlicher, S., Argoul, P.: Modal identification of linear non-proportionally damped systems by wavelet transform. Mechanical Systems and Signal Processing 21(3), 1386–1421 (2007)CrossRefGoogle Scholar
  8. 8.
    Hasan, M.K., Chowdhury, A.K.M.Z.R., Khan, M.R.: Identification of Autoregressive Signals in Colored Noise Using Damped Sinusoidal Model. IEEE Transactions on Circuits and Systems-I: Fundamental Theory and Applications 50(7), 966–969 (2003)CrossRefGoogle Scholar
  9. 9.
    Ho, K.C., Blunt, S.D.: Adaptive sparse system identification using wavelets. IEEE Transactions on Circuits and Systems II - Analog and Digital Signal Processing 49(10), 656–667 (2002)CrossRefGoogle Scholar
  10. 10.
    Huang, K., Aviyente, S.: Information-theoretic wavelet packet subband selection for texture classification. Signal Processing 86(7), 1410–1420 (2006)zbMATHCrossRefGoogle Scholar
  11. 11.
    Huang, C.S., Su, W.C.: Identification of modal parameters of a time invariant linear system by continuous wavelet transformation. Mechanical Systems and Signal Processing 21(4), 1642–1664 (2007)CrossRefMathSciNetGoogle Scholar
  12. 12.
    Ljung, L.: System Identification: Theory for the User, 2nd edn. Prentice-Hall, Englewood Cliffs (1999)Google Scholar
  13. 13.
    Paiva, H.M., Galvao, R.K.H.: Wavelet-packet identification of dynamic systems in frequency subbands. Signal Processing 86(8), 2001–2008 (2006)zbMATHCrossRefGoogle Scholar
  14. 14.
    Vera-Candeas, P., Ruiz-Reyes, N., Rosa-Zurera, M., Cuevas-Martnez, J.C., Lpez-Ferreras, F.: Sparse Approximations in Signal and Image Processing. Signal Processing 86(3), 432–443 (2006)zbMATHCrossRefGoogle Scholar
  15. 15.
    Vetterli, M., Kovacevic, J.: Wavelets and Subband Coding. Prentice-Hall, Upper Saddle River (1995)zbMATHGoogle Scholar
  16. 16.
    Zheng, W.X.: Estimation of the Parameters of Autoregressive Signals From Colored Noise-Corrupted Measurements. IEEE Signal Processing Letters 7(7), 201–204 (2000)CrossRefGoogle Scholar
  17. 17.
    Zheng, Y., Tay, D.B.H., Lin, Z.: Modeling general distributed nonstationary process and identifying time-varying autoregressive system by wavelets: theory and application. Signal Processing 81, 1823–1848 (2001)zbMATHCrossRefGoogle Scholar
  18. 18.
    Zheng, W.X.: Parametric Identification of Linear Systems Operating Under Feedback Control. IEEE Transactions on Circuits and Systems-I: Fundamental Theory and Applications 48(4), 451–458 (2001)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Henrique Mohallem Paiva
    • 1
  • Roberto Kawakami Harrop Galvão
    • 2
  1. 1.Empresa Brasileira de AeronáuticaEMBRAERSão José dos CamposBrazil
  2. 2.Instituto Tecnológico de AeronáuticaITA, CTASão José dos CamposBrazil

Personalised recommendations