• DocumentCode
    1844812
  • Title

    General method for empirical data decomposition filtering design

  • Author

    Wu Xiaoqin ; Guo Zhen ; Zhang Hongke

  • Author_Institution
    Electr. Inf. Eng. Coll., Beijing Jiaotong Univ., Beijing, China
  • Volume
    1
  • fYear
    2012
  • fDate
    21-25 Oct. 2012
  • Firstpage
    732
  • Lastpage
    736
  • Abstract
    Based on the research on Empirical Data Decomposition (EDD), the structure for Empirical Data Decomposition is proposed in which the high pass filter is composed of a predictor and an adder. In terms of reconstructing requirement, the filter design rule is presented when the EDD analysis filter and synthesis filter are restricted as FIR filter. The relationship between equivalent synthesis filter and analysis filter is also presented. Finally the structure for the synthesis filter is discussed. Except for the FIR requirement, there is no additional restriction for the predictor, so the filter can be easily designed to satisfy different requirements. EDD is suitable not only for stationary data analysis or piece-wise stationary data analysis but also for non-stationary data analysis.
  • Keywords
    FIR filters; adders; decomposition; high-pass filters; prediction theory; signal reconstruction; signal synthesis; EDD; adder; empirical data decomposition filtering design; equivalent FIR filter synthesis; high pass filter; nonstationary data analysis; piecewise stationary data analysis; predictor; signal reconstruction; EDD; FIR; Non-stationary Data Analysis; filter design;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Signal Processing (ICSP), 2012 IEEE 11th International Conference on
  • Conference_Location
    Beijing
  • ISSN
    2164-5221
  • Print_ISBN
    978-1-4673-2196-9
  • Type

    conf

  • DOI
    10.1109/ICoSP.2012.6491591
  • Filename
    6491591