IDENTIFICATION OF LINEAR SAMPLED DATA SYSTEMS

Abstract

A least squares estimator is derived for the state transition matrix phi of a linear, stationary sampled data system operating in a stochastic environment. The estimator is shown to be unbiased and minimum variance under the condition of full observability of the state vector of the system. The estimator is also shown to be the Maximum Likelihood Estimator for the case of the stochastic environment having Gaussian statistics. The estimation scheme is compared with two other recently published estimation schemes, both of which are shown to be special cases of the scheme herein presented.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1967
Accession Number
AD0820396

Entities

People

  • Ronald K. Blackner

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Air Platforms

DTIC Thesaurus Topics

  • Algorithms
  • Computational Science
  • Data Science
  • Difference Equations
  • Differential Equations
  • Environment
  • Equations
  • Estimators
  • Information Science
  • Jet Transport Aircraft
  • Mathematical Models
  • Mathematics
  • Models
  • Sampling
  • Statistical Algorithms
  • Statistics
  • Transitions

Fields of Study

  • Mathematics

Readers

  • Mathematical Modeling and Probability Theory.
  • Statistical inference.