A SUBOPTIMAL NONLINEAR ESTIMATOR FOR SYSTEMS WITH UNKNOWN PARAMETERS,

Abstract

An algorithm is presented which provides a maximum likelihood estimate for an unknown parameter contained in a linear-dynamic system driven by white, gaussian noise. The observations of the system are also corrupted by white noise. Taylor series expansions are used to develop approximations to the estimation equations. These approximations are recursive and can be calculated iteratively. The algorithm can be realized either as an analog or as a digital system and is shown to compare favorably with existing techniques in a simple example. (Author)

Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1970
Accession Number
AD0712894

Entities

People

  • Richard Stanton Brownell

Organizations

  • University of Illinois Urbana–Champaign

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Equations
  • Estimators
  • Gaussian Noise
  • Mathematics
  • Noise
  • Observation
  • Statistical Algorithms
  • White Noise

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.