An Investigation of the Optimal Sensor Ensemble for Sensor Fusion

Abstract

This thesis continues the research begun by Storm, Bauer and Oxley in 2003 into the fusion of classifiers. It examines the fusion of up to three correlated classifiers using three different fusion techniques. The overall objective was to determine the optimal ensemble of classifiers to maximize the expected classification accuracy. The ISOC fusion method (Haspert, 2000), the ROC Within fusion method (Oxley and Bauer, 2002) and a Probabilistic Neural Network were the three fusion techniques employed in these set of experiments. Performance of the classifiers and the fusion methods is measured via ROC curves. Two possible configurations of feature correlations were examined. The expected true positive value relative to a prior distribution of correlation levels for each configuration was then used to compare the classifiers and the fused classifiers performance and thereby allowing for the selection of an optimal ensemble.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2004
Accession Number
ADA422890

Entities

People

  • Paul P. Clemans

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Engineered Resilient Systems
  • Human Systems
  • Materials and Manufacturing Processes
  • Sensors
  • Weapons Technologies

DTIC Thesaurus Topics

  • Accuracy
  • Air Force
  • Airborne Warning And Control System
  • Artificial Intelligence
  • Automated Target Recognition
  • Classification
  • Command And Control
  • Data Science
  • Department Of Defense
  • Detectors
  • Identification Systems
  • Information Science
  • Literature Surveys
  • Machine Learning
  • Neural Networks
  • Sensor Fusion
  • Target Recognition

Readers

  • Approximation Theory.
  • Computer Vision.
  • Sensor Fusion and Tracking Systems.

Technology Areas

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