• DocumentCode
    831807
  • Title

    Kalman filtering and Riccati equations for descriptor systems

  • Author

    Nikoukhah, Ramine ; Willsky, Alan S. ; Levy, Bernard C.

  • Author_Institution
    Inst. Nat. de Recherche en Inf. et en Autom., Rocquencourt, Le Chesnay, France
  • Volume
    37
  • Issue
    9
  • fYear
    1992
  • fDate
    9/1/1992 12:00:00 AM
  • Firstpage
    1325
  • Lastpage
    1342
  • Abstract
    A general formulation of a discrete-time filtering problem for descriptor systems is considered. It is shown that the nature of descriptor systems leads directly to the need to examine singular estimation problems. Using a dual approach to estimation, the authors derive a so-called 3-block form for the optimal filter and a corresponding 3-block Riccati equation for a general class of time-varying descriptor models which need not represent a well-posed system in that the dynamics may be either over or under constrained. Specializing in the time-invariant case, they examine the asymptotic properties of the 3-block filter, and in particular analyze in detail the resulting 3-block algebraic Riccati equation. The noncausal nature of discrete-time descriptor dynamics implies that future dynamics may provide some information about the present state. A modified form for the descriptor Kalman filter that takes this information into account is presented
  • Keywords
    Kalman filters; algebra; discrete time systems; filtering and prediction theory; maximum likelihood estimation; 3-block algebraic Riccati equation; 3-block form; Kalman filtering; Riccati equations; discrete-time descriptor dynamics; discrete-time filtering; optimal filter; singular estimation problems; time-varying descriptor models; time-varying models; Extraterrestrial phenomena; Filtering; Gaussian noise; Indexing; Kalman filters; Laboratories; Random variables; Recursive estimation; Riccati equations; Time varying systems;
  • fLanguage
    English
  • Journal_Title
    Automatic Control, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0018-9286
  • Type

    jour

  • DOI
    10.1109/9.159570
  • Filename
    159570