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A New Approach for Filtering and Derivative Estimation of Noisy Signals

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Abstract

Filtering and estimation of derivatives in a single step from noisy signals is an important and challenging task in signal processing. The aim of this paper is to propose a new tracking differentiator based on only one parameter; this differentiator is able to synchronously filter noise and estimate the derivative of the input signal. The new tracking differentiator design is based on an inverse Taylor series approach. Both error and stability analyses of the tracking differentiator design are provided. Theoretical analysis and computer simulation results show that this tracking differentiator cannot only obtain better filtering results than previous approaches but can also estimate the derivatives with high accuracy.

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References

  1. A.T. Bahill, J.D. McDonald, Frequency limitations and optimal step size for the two-point central difference derivative algorithm with applications to human eye movement data. IEEE Trans. Biomed. Eng. BME-30(3), 191–194 (1983)

    Article  Google Scholar 

  2. L.M. Bruce, J. Li, Wavelets for computationally efficient hyperspectral derivative analysis. IEEE Trans. Geosci. Remote Sens. 39(7), 1540–1546 (2001)

    Article  Google Scholar 

  3. M. D’Amico, G. Ferrigno, Comparison between the more recent techniques for smoothing and derivative assessment in biomechanics. Med. Biol. Eng. Comput. 30(2), 193–204 (1992)

    Article  Google Scholar 

  4. J.Q. Han, W. Wang, Nonlinear tracking-differentiators. J. Syst. Sci. Math. Sci. 14(2), 177–183 (1994)

    MATH  Google Scholar 

  5. J.D. Han, Z. Jiang, Y.Y. Nie, Numerical stabilization of polynomial and matrix. IMA J. Math. Control Inf. 24(4), 473–482 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  6. I.Z. Kiss, Q. Lv, J.L. Hudson, Synchronization of non-phase-coherent chaotic electrochemical oscillations. Phys. Rev. E 71(3), 035201 (2005)

    Article  Google Scholar 

  7. A. Levant, Robust exact differentiation via sliding mode technique. Automatica 34(3), 379–384 (1998)

    Article  MATH  MathSciNet  Google Scholar 

  8. X. Lu, J.K. Hedrick, Inverse Taylor series problem in linear filtering and related conjectures, in Proc. American Control Conference, Arlington, VA (2001), pp. 2528–2529

    Google Scholar 

  9. Y.Y. Nie, X.K. Xie, New criteria for polynomial stability. IMA J. Math. Control Inf. 4, 1–12 (1987)

    Article  MATH  MathSciNet  Google Scholar 

  10. A.S. Poznyak, E.N. Sanchez, O. Palma, W. Yu, Robust asymptotic neuro observer with time delay term, in Proc. IEEE ISIC 2000, Rio. Patras. Greece (2000), pp. 19–24

    Google Scholar 

  11. A. Savitzky, M.J.E. Golay, Smoothing and differentiation of data by simplified least squares procedures. Anal. Chem. 36(8), 1627–1639 (1964)

    Article  Google Scholar 

  12. L.K. Vasiljevic, H.K. Khalil, Differentiation with high-gain observers the presence of measurement noise, in Proc. 45th IEEE CDC, San Diego, USA (2006), pp. 4717–4722

    Google Scholar 

  13. X. Wang, Design and analysis for new discrete tracking differentiators. Appl. Math. J. Chin. Univ. Ser. B 18(2), 214–222 (2003)

    Article  MATH  Google Scholar 

  14. S. Yadir, M. Benhmida, M. Sidki, E. Assaid, M. Khaidar, New method for extracting the model physical parameters of solar cells using explicit analytic solutions of current-voltage equation, in Proc IEEE ICM, Marrakech, Morocco (2009), pp. 390–393

    Google Scholar 

  15. H.Q. Yang, B.Y. Kuang, A.M. Mouazen, Affect of different preprocessing methods on principal component analysis for soil classification, in Proc. IEEE ICMTMA’11, vol. 1, Washington, USA (2011), pp. 355–358

    Google Scholar 

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Acknowledgements

This work was supported in part by the Fundamental Research Funds for the Central Universities (N110423006).

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Correspondence to Z. G. Li.

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Li, Z.G., Ma, Z.H. A New Approach for Filtering and Derivative Estimation of Noisy Signals. Circuits Syst Signal Process 33, 589–598 (2014). https://doi.org/10.1007/s00034-013-9634-z

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  • DOI: https://doi.org/10.1007/s00034-013-9634-z

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