Effects of Uncertainty on Real World Aerospace Mission Data

Abstract

Uncertainty is an age old element of warfare. How does one measure success or failure on the battlefield? This thesis explores the effect of uncertainty on real world aerospace mission data from Operation Iraqi Freedom (OIF). Sources of uncertainty are identified during data preparation and the effects of uncertainty are investigated using multivariate analysis techniques. Two distinct cases are analyzed: one case with a low level of uncertainty in predicting whether or not a mission is pre-planned or alert generated, and another case with a high level of uncertainty in predicting whether or not a mission is a success or a failure. Three multivariate analysis techniques are used on both cases: Signal-to-Noise Ratio (SNR) saliency, Feed Forward Neural Network (FFNN) supervised training, and General Regression Neural Network (GRNN) supervised training. Measures of performance are gathered from each of these techniques and analyzed to determine the effect of uncertainty on the data.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2004
Accession Number
ADA422951

Entities

People

  • Steven J. Barosko

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Electronic Warfare
  • Engineered Resilient Systems
  • Ground and Sea Platforms
  • Space
  • Weapons Technologies

DTIC Thesaurus Topics

  • Air Defense
  • Air Force
  • Combat Operations
  • Computational Science
  • Databases
  • Information Science
  • Iraqi-War
  • Literature Surveys
  • Military Operations
  • Multivariate Analysis
  • Munitions
  • Neural Networks
  • Spreadsheet Software
  • Statistical Analysis
  • Training
  • War
  • Warfare

Readers

  • Acoustical Oceanography.
  • Computational Modeling and Simulation
  • Joint Military Operations and Doctrine.

Technology Areas

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