Statistical Signal Processing Using a Class of Iterative Estimation Algorithms.

Abstract

Many Signal Processing problems may be posed as statistical parameter estimation problems. A desired solution for the statistical problem is obtained by maximizing the Likelihood(ML), the A-Posteriori probability (MAP) or by optimizing other criterion, depending on the a-priori knowledge. However, in many practical situations, the original signal processing problem may generate a complicated optimization problem e.g. when the observed signals are noisy and 'incomplete'. A framework of iterative procedures for maximizing the likelihood, the EM algorithm, is widely used in statistics. In the EM algorithm, the observations are considered 'incomplete' and the algorithm iterates between estimating the sufficient statistics of the 'complete data' given the observations and a current estimate of the parameters (the E step) and maximizing the likelihood of the complete data, using the estimated sufficient statistics (the M step). When this algorithm is applied to signal processing problems, it yields, in many cases, an intuitively appealing processing scheme.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1987
Accession Number
ADA189050

Entities

People

  • Meir Feder

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Computational Science
  • Data Science
  • Electrical Engineering
  • Information Processing
  • Information Science
  • Information Theory
  • Least Squares Method
  • Mathematical Filters
  • Mathematical Models
  • Network Science
  • Random Variables
  • Signal Processing
  • Statistical Algorithms
  • Statistical Analysis
  • Statistical Estimation
  • Stochastic Processes

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