• DocumentCode
    3861529
  • Title

    Robust estimation with unknown noise statistics

  • Author

    Z.M. Durovic;B.D. Kovacevic

  • Author_Institution
    Fac. of Electr. Eng., Belgrade Univ., Serbia
  • Volume
    44
  • Issue
    6
  • fYear
    1999
  • Firstpage
    1292
  • Lastpage
    1296
  • Abstract
    The equivalence between the Kalman filter and a particular least squares regression problem is established and the regression problem is solved robustly using a statistical approach, named M-estimation. M-robust estimators are derived for adaptive estimation of the unknown a priori state and observation noise statistics simultaneously with the system states. The feasibility of the approach is demonstrated with simulation.
  • Keywords
    "Noise robustness","Statistics","Least squares approximation","State estimation","Gaussian noise","Adaptive estimation","Noise generators","Adaptive filters","Estimation theory","Probability distribution"
  • Journal_Title
    IEEE Transactions on Automatic Control
  • Publisher
    ieee
  • ISSN
    0018-9286
  • Type

    jour

  • DOI
    10.1109/9.769393
  • Filename
    769393
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3861529