Modeling and Recursive Estimation of Two Dimensional Random Fields and Applications to Target Detection,

Abstract

First an investigation of modeling stochastic processes by difference equations (Markov process) was undertaken. The starting point of the modeling procedure is the knowledge of the spectrum of the process. Two methods are discussed. One is based on optimal estimation theory and leads in most cases to a high-order (perhaps infinite) Markov process. The second method, based on linear system theory, leads to a first order Markov process (in matrix representation). Both methods have been extended to two-dimensional processes. Secondly, recursive estimation (filtering) of two-dimensional random fields was addressed. It was shown that a two-dimensional recursive filter cannot be optimal. Therefore, only a sub-optimal solution is available. This solution minimizes the mean square error for a specific structure of a filter. Finally, applications of modeling and recursive filtering are discussed. An image that includes a target, correlated noise and random noise was processed. Some methods of target enhancement (also called 'restoration' are discussed. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1977
Accession Number
ADA045179

Entities

People

  • Moshe Shachar

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • C4I
  • Energy and Power Technologies
  • Space
  • Weapons Technologies

DTIC Thesaurus Topics

  • Computational Science
  • Detection
  • Difference Equations
  • Differential Equations
  • Electrical Engineering
  • Engineering
  • Equations
  • Filtration
  • Image Processing
  • Kalman Filters
  • Markov Processes
  • Mathematical Filters
  • Probability
  • Random Variables
  • Recursive Filters
  • Stochastic Processes
  • Two Dimensional

Fields of Study

  • Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Image Processing and Computer Vision.