Multichannel 2-D Power Spectral Estimation and Applications.

Abstract

Spectral estimation for multiple 2-D signals by model-based methods is developed. The procedures compute the entire spectral matrix of autospectra and cross spectra for the set of 2-D signals. Spectral analysis by autoregressive (AR) modeling is studied extensively. Specific differences between AR models for this problem and those for lower dimensional problems are highlighted. An extension of the Jackson-Chien method for combining estimates with single quadrant support is proposed and a method is developed for estimating the model parameters directly from the data (i.e. without prior estimation of a correlation matrix). A measure of the similarity of two spectral estimates based on the statistical divergence is proposed and used to compare various spectral estimates. Keywords: Signal modeling, Linear prediction, Image coding, Maximum likelihood method, Spectral estimation.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1987
Accession Number
ADA191273

Entities

People

  • Hamdy T. El-shaer

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Air Platforms
  • Energy and Power Technologies
  • Space

DTIC Thesaurus Topics

  • Algorithms
  • Computational Science
  • Data Compression
  • Databases
  • Differential Equations
  • Digital Images
  • Electrical Engineering
  • Engineering
  • Image Processing
  • Information Theory
  • Mathematical Filters
  • Power Spectra
  • Remotely Piloted Vehicles
  • Signal Processing
  • Spectrum Analysis
  • Three Dimensional
  • Two Dimensional

Fields of Study

  • Engineering

Readers

  • Image Processing and Computer Vision.
  • Statistical inference.