Filtering and Prediction Performance for a Class of Systems with Uncertain Parameters.

Abstract

A covariance analysis technique using the Cramer-Rao lower bound for assessing filtering and prediction performance for a class of nonlinear systems is presented. The class of systems considered is nonlinear, deterministic, with unknown parameters. The validity of this technique for the problems considered is justified using local observability theory and unbiased estimation for nonlinear systems. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Jul 27, 1984
Accession Number
ADA146145

Entities

People

  • C. B. Chang
  • K. P. Dunn

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Weapons Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Coefficients
  • Computations
  • Covariance
  • Differential Equations
  • Equations
  • Estimators
  • Filtration
  • Kalman Filters
  • Linear Systems
  • Mathematical Filters
  • Maximum Likelihood Estimation
  • Measurement
  • Nonlinear Systems
  • Riccati Equation
  • Trajectories

Fields of Study

  • Engineering

Readers

  • Statistical inference.