Kalman Filtering and Riccati Equations for Descriptor Systems

Abstract

In this paper we consider a general formulation of a discrete-time filtering problem for descriptor systems. 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 we 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 to the time-invariant case we examine the asymptotic properties of the 3-block filter, and in particular analyze in detail the resulting 3-block algebraic Riccati equation, generalizing significantly the results in [23, 28, 33]. Finally, the noncausal nature of discrete-time descriptor dynamics implies that future dynamics may provide some information about the present state. We present a modified form for the descriptor Kalman filter that takes this information into account.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1991
Accession Number
ADA458383

Entities

People

  • Alan S. Willsky
  • Bernard C. Lévy
  • Ramine Nikoukhah

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Air Platforms
  • Autonomy
  • C4I

DTIC Thesaurus Topics

  • Boundaries
  • Computations
  • Equations
  • Estimators
  • Filters
  • Filtration
  • Kalman Filtering
  • Kalman Filters
  • Military Research
  • Noise
  • Numbers
  • Observation
  • Random Variables
  • Riccati Equation
  • Standards
  • Steady State
  • Two Dimensional

Fields of Study

  • Mathematics

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.