Accession Number:

ADA053145

Title:

Consistency of a Class of Robust Estimators of Crosscorrelation.

Descriptive Note:

Final rept.,

Corporate Author:

JOHNS HOPKINS UNIV BALTIMORE MD

Personal Author(s):

Report Date:

1978-03-31

Pagination or Media Count:

25.0

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 F1-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.

Subject Categories:

  • Statistics and Probability
  • Cybernetics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE