Consistency of a Class of Robust Estimators of Crosscorrelation.

Abstract

In a previous work, a class of non-linear recursive estimators of cross-correlation was shown through simulations to be robust in identifying the parameters of linear, stationary, single-input-single-output systems whose output measurements are contaminated by noise which is not completely specified: the measurement noise distribution f is given by F=(1-epsilon) K + epsilon C, where K is completely known and C belongs to the class of zero-mean, symmetric, finite variance distributions. The principal result of this report is a complement to these earlier results -- namely, a proof of consistency, with mean square convergence, for a general sub-class of the non-linear estimators of crosscorrelation defined in the earlier work. The proof is along the same lines as those followed in the Robbins-Monro stochastic approximation method.

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Document Details

Document Type
Technical Report
Publication Date
Mar 31, 1978
Accession Number
ADA053145

Entities

People

  • V. David Vandelinde

Organizations

  • Johns Hopkins University

Tags

Communities of Interest

  • Human Systems
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Convergence
  • Cross Correlation
  • Estimators
  • Linear Systems
  • Measurement
  • New York
  • Probability
  • Probability Distributions
  • Random Variables
  • Security
  • Simulations
  • Stationary
  • Statistical Algorithms
  • Stochastic Processes
  • Students
  • Universities

Fields of Study

  • Mathematics

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Approximation Theory.
  • Mathematical Modeling and Probability Theory.