An Investigation of the Effects of Correlation in Sensor Fusion

Abstract

This thesis takes the first step towards the creation of a synthetic classifier fusion-testing environment. The effects of data correlation on three classifier fusion techniques were examined. The three fusion methods tested were the ISOC fusion method (Haspert, 2000), the ROC "Within" Fusion method (Oxley and Bauer, 2002) and the simple use of a Probabilistic Neural Network (PNN) as a fusion tool. Test situations were developed to allow the examination of various levels of correlation both between and within feature streams. The effects of training a fusion ensemble on a common dataset versus an independent data set were also contrasted. Some incremental improvements to the ISOC procedure were discovered in this process.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2003
Accession Number
ADA412745

Entities

People

  • Susan A. Storm

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Materials and Manufacturing Processes
  • Sensors
  • Weapons Technologies

DTIC Thesaurus Topics

  • Accuracy
  • Air Force
  • Algorithms
  • Data Analysis
  • Data Science
  • Data Sets
  • Detection
  • Discriminant Analysis
  • Identification Systems
  • Information Science
  • Literature Surveys
  • Machine Learning
  • Neural Networks
  • Sensor Fusion
  • Training
  • United States
  • Warfare

Readers

  • Neural Network Machine Learning.
  • Regression Analysis.
  • Sensor Fusion and Tracking Systems.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Neural Networks