Autoregressive Spectral Estimation in Additive Noise

Abstract

The estimation of the spectral density of a discrete-time stationary Gaussian autoregressive (AR) process from a finite set of noisy observations is considered. A modified spectral estimator based on the high-order Yule-Walker equations is considered. Joint asymptotic normally of this spectral estimator is established; a precise asymptotic expression for the covariance matrix of the limiting distribution is obtained. The special case of AR(1) plus noise is considered in some detail. Keywords: Bearing estimation; Array processing; Statistics; Time series analysis, Reprints.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1989
Accession Number
ADA208084

Entities

People

  • Donald F. Gingras
  • Elias Masry

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Abstracts
  • Acoustics
  • Asymptotic Normality
  • Covariance
  • Data Science
  • Electrical Engineering
  • Equations
  • Estimators
  • Information Processing
  • Information Science
  • Normality
  • Probability
  • Random Variables
  • Signal Processing
  • Statistical Algorithms
  • Statistics
  • Stochastic Processes

Fields of Study

  • Engineering

Readers

  • Statistical inference.