Application of Statistical Estimation Procedures to the Identification Problem

Abstract

The method of maximum likelihood parameter estimation is applied to the problem of measuring parameters of an unknown linear filter or control system from input-output data when it is assumed that the output signal is corrupted with an additive Gaussian noise signal. The physical realization suggested by the integral formulation of the estimation technique is discussed and illustrated. Approximate expressions for the parameter estimates and the covariance matrix of the errors in the parameter estimates are obtained in the strong signal case. This analysis also has applications to the adaptive radar problem.

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Document Details

Document Type
Technical Report
Publication Date
Nov 03, 1961
Accession Number
AD0266877

Entities

People

  • J. C. Lindenlaub
  • R. P. Wishner

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • C4I
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Adaptive Control Systems
  • Control Systems
  • Control Systems Engineering
  • Covariance
  • Data Science
  • Engineering
  • Engineers
  • Filters
  • Identification
  • Information Science
  • Integrals
  • Maximum Likelihood Estimation
  • Noise
  • Signal Detection
  • Statistical Estimation
  • Statistical Inference
  • White Noise

Fields of Study

  • Engineering

Readers

  • Computational Modeling and Simulation
  • Statistical inference.