• DocumentCode
    1333171
  • Title

    Polynomial prediction using incomplete data

  • Author

    Harju, P.T.

  • Author_Institution
    Lab. of Telecommun. Technol., Helsinki Univ. of Technol., Espoo
  • Volume
    45
  • Issue
    3
  • fYear
    1997
  • fDate
    3/1/1997 12:00:00 AM
  • Firstpage
    768
  • Lastpage
    770
  • Abstract
    We derive an FIR polynomial predictor for data in which some samples are missing. The method is compared with a computationally lighter algorithm that is based on decision-driven recursion. Both schemes are found to perform almost identically well on predicting a sinusoidal signal corrupted by both impulsive and Gaussian noise
  • Keywords
    FIR filters; Gaussian noise; computational complexity; digital filters; polynomials; prediction theory; signal sampling; FIR polynomial predictor; Gaussian noise; decision-driven recursion; impulsive noise; incomplete data; polynomial prediction; sinusoidal signal; Additive noise; Autocorrelation; Filtering; Finite impulse response filter; Gaussian noise; Polynomials; Predictive models; Radio communication; Signal processing; Signal processing algorithms;
  • fLanguage
    English
  • Journal_Title
    Signal Processing, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1053-587X
  • Type

    jour

  • DOI
    10.1109/78.558500
  • Filename
    558500
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1333171