An Investigation of the Effects of Correlation and Autocorrelation in Classifier Fusion with Non-Declarations

Abstract

Air Force doctrine requires reliable and accurate information when striking targets. Further, this doctrine states that fusion should be utilized whenever possible to ensure the best possible information is conveyed; there is no specific guidance as to how to fuse this information. This thesis extends the research found in Leap, Bauer, and Oxley (2004) to include a non-declared class. The Identification system operating characteristic (ISOC) was adapted to allow for non-declarations both at the individual sensor level as well as the fused output level. A probabilistic neural network (PNN) was also used as a fusion technique. A cost function was developed that incorporated misclassification error as well as non-declaration rules. In addition, a heuristic was developed to find optimal rules through a likelihood ratio method. Finally, a sensitivity analysis was performed.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2005
Accession Number
ADA437556

Entities

People

  • Frank M. Mindrup

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • C4I
  • Energy and Power Technologies
  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Air Force
  • Artificial Intelligence
  • Command And Control
  • Data Science
  • Detectors
  • Doctrine
  • Identification
  • Identification Systems
  • Information Science
  • Literature Surveys
  • Machine Learning
  • Neural Networks
  • Signal Processing
  • Target Recognition
  • Three Dimensional
  • United States
  • Warfare

Readers

  • Joint Military Operations and Doctrine.
  • Regression Analysis.
  • Sensor Fusion and Tracking Systems.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - DoD AI Strategy