LINEAR ESTIMATION OF SAMPLED STOCHASTIC PROCESSES WITH RANDOM PARAMETERS

Abstract

In this investigation the geneal solution is derived for the problem of the optimum linear estimation of a sampled stochastic process, when the transition and output matrices of the model of the process are random parameters that are independent from one sample point to the next with known mean and covariance. The resulting estimate is optimum in the sense that it minimizes the trace of the covariance matrix of the error (a generalized mean-squared-error criterion). All of these results are derived from the sampled version of the Wiener-Hopf equation, and they apply without modification to stationary and nonstationary statistics and to growing-memory and infinite-memory filters.

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 1962
Accession Number
AD0441527

Entities

People

  • H. E. Rauch

Organizations

  • Stanford University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Air Force
  • Data Science
  • Difference Equations
  • Differential Equations
  • Distribution Functions
  • Information Science
  • Jet Propulsion
  • Linear Differential Equations
  • Markov Processes
  • Normal Distribution
  • Probability Distributions
  • Random Variables
  • Stationary Processes
  • Statistical Samples
  • Statistical Sampling
  • Steady State
  • Stochastic Processes

Fields of Study

  • Mathematics

Readers

  • Approximation Theory.
  • Linear Algebra
  • Statistical inference.