Parameter Estimation in Stochastic Differential Systems: Theory and Application.

Abstract

This paper presents a theory of estimation of parameters in linear stochastic differential equations based on time-continuous observation. It uses a white noise model to represent observation errors (in contrast to a Wiener process model). The application is to the problem of identifying aircraft as well as turbulence (wind-gust) parameters from flight test data. Results obtained on actual data are presented.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1977
Accession Number
ADA039182

Entities

People

  • A.V. Balakrishnan

Organizations

  • University of California, Los Angeles

Tags

Communities of Interest

  • Air Platforms
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Additives (Chemicals)
  • Aircrafts
  • Banach Space
  • Bandwidth
  • Computational Science
  • Computer Simulations
  • Differential Equations
  • Digital Computers
  • Equations
  • Hilbert Space
  • Identities
  • Noise
  • Partial Differential Equations
  • Probability
  • Random Variables
  • Stochastic Processes
  • White Noise

Readers

  • Aerospace Engineering
  • Climatology
  • Theoretical Analysis.