Independent Component Analysis by Entropy Maximization (INFOMAX)

Abstract

This thesis explores the "Infomax" method of Independent Component Analysis (ICA) to accomplish blind source separation (BSS). The Infomax method separates unknown source signals from a number of signal mixtures by maximizing the entropy of a transformed set of signal mixtures and is accomplished by performing gradient ascent in MATLAB. The thesis specifically focuses on small numbers of two types of signals: audio signals and simple communications signals (polar non-return to zero signals). The Infomax method is found to be successful and efficient only for small numbers of signals, and improvements to the gradient ascent algorithm should be made for the Infomax algorithm to succeed for more than three signal mixtures. MATLAB implementation code is included as appendices.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2007
Accession Number
ADA470123

Entities

People

  • Jennie H. Garvey

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Advanced Electronics

DTIC Thesaurus Topics

  • Algorithms
  • Code Division Multiple Access
  • Computations
  • Economic Analysis
  • Engineering
  • Frequency
  • Information Theory
  • Multiple Access
  • Notation
  • Probability
  • Probability Density Functions
  • Random Variables
  • Signal Processing
  • Simulations
  • Statistical Functions
  • United States
  • United States Naval Academy

Fields of Study

  • Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Radio communications and signal processing.