• DocumentCode
    1118175
  • Title

    Adaptive Detection and Removal of Non-Gaussian Spikes from Gaussian Data

  • Author

    Boucher, R.E. ; Noonan, J.P.

  • Author_Institution
    Bedford Research Associates, Bedford, MA 01730.
  • Issue
    2
  • fYear
    1982
  • fDate
    3/1/1982 12:00:00 AM
  • Firstpage
    132
  • Lastpage
    136
  • Abstract
    A nonlinear adaptive method is presented for filtering a signal which is corrupted by spikes which take discrete values Mi with probability Pi at random points in time. An unsupervised learning technique is used to estimate the unknown parameters Mi, Pi, and oi. The spikes are then removed using a Bayes classifier. A theoretical and experimental comparison with the MMSE linear filter is presented.
  • Keywords
    Adaptive filters; Circuit noise; Digital filters; Filtering; Gaussian noise; Hardware; Kalman filters; Noise cancellation; Nonlinear filters; Signal processing; Bayes classifier; Poisson process; maximum likelihood estimation; minimum mean-square error linear filter; mixture density; noise cancellation; unsupervised learning;
  • fLanguage
    English
  • Journal_Title
    Pattern Analysis and Machine Intelligence, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0162-8828
  • Type

    jour

  • DOI
    10.1109/TPAMI.1982.4767218
  • Filename
    4767218
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1118175