• DocumentCode
    799116
  • Title

    Stochastic peak tracking and the Kalman filter

  • Author

    Chang, Silvia

  • Author_Institution
    State University of New York, Stony Brook, NY, USA
  • Volume
    13
  • Issue
    6
  • fYear
    1968
  • fDate
    12/1/1968 12:00:00 AM
  • Firstpage
    750
  • Lastpage
    750
  • Abstract
    The peak tracking problem can be reduced to a Kalman falter problem [1] with the additional variable of the excursion amplitude c , which is then obtained by maximizing the expected peak. In the special case where the parameters do not change, the method yields two tracking procedures depending on the criterion used: 1) Tracking for a limited time and then settling for the parameter value so determined. It is shown that the expected error is proportional to t-1, where t is the tracking time [2]. 2) A procedure which agrees with the Kiefer-Wolfowitz stochastic approximation method [3]. It is shown further that the expected total reduction in peak value (due to error and hunting loss) is proportional to t^{-1/2}.
  • Keywords
    Kalman filtering; Stochastic processes; Tracking filters; Approximation methods; Equations; Kalman filters; Maximum likelihood detection; Optimal control; Stochastic processes; Yield estimation;
  • fLanguage
    English
  • Journal_Title
    Automatic Control, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0018-9286
  • Type

    jour

  • DOI
    10.1109/TAC.1968.1099058
  • Filename
    1099058