Recursive AR Spectral Estimation.

Abstract

Recursive least squares spectral estimation algorithms have attracted much attention recently because of their excellent convergence behavior and fast parameter tracking capability. They are recursive in the sense that as a new element of the time series is observed, the parameters of a spectral estimation model are algorithmically updated. Recently, a recursive algorithm for efficiently obtaining an autoregressive (AR) spectral estimate has been introduced by Morf and Lee. In this paper a more insightful development of their technique is presented. A modification of the data, namely prewindowing, is applied to achieve a significant computational improvement. The development is predicated on utilization of a projection operator. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1980
Accession Number
ADA098488

Entities

People

  • James A. Cadzow
  • Koji Ogino

Organizations

  • Virginia Tech

Tags

Communities of Interest

  • Ground and Sea Platforms
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Coefficients
  • Computing-Related Activities
  • Data Science
  • Decomposition
  • Electrical Engineering
  • Engineering
  • Equations
  • Information Processing
  • Information Science
  • Least Squares Method
  • Numbers
  • Observation
  • Signal Processing
  • Statistical Analysis
  • Statistics
  • Vector Spaces

Fields of Study

  • Engineering

Readers

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