Multidimensional Spectral Estimation Using Iterative Methods

Abstract

This thesis treats the topic of multi-dimensional autoregressive (AR) spectral estimation. An iterative algorithm for the solution of toeplitz block- toeplitz matrix equations is presented. This leads to a fast solution of the two dimensional normal equation compared with direct inversion of the autocorrelation matrix. The covariance method is used to estimate the autocorrelation function. Because the resulting matrix is not toeplitz block- toeplitz, a modified iterative algorithm is presented. Quarter-plane and nonsymmetric half-plane support are used, as well as combined quadrant averaging. Results of computer simulation show that in some cases a single iteration is sufficient to produce an acceptable spectral estimate. Because the AR parameters are estimated from previous values, this suggests the possibility to estimate spectral densities of slowly varying random processes.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1990
Accession Number
ADA237025

Entities

People

  • Roderick C. Wester

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Air Platforms
  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Classification
  • Computers
  • Covariance
  • Data Sets
  • Electrical Engineering
  • Engineering
  • Equations
  • Image Processing
  • Inversion
  • Iterations
  • Quadrants
  • Security
  • Signal Processing
  • Two Dimensional
  • United States
  • White Noise

Fields of Study

  • Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Linear Algebra