BDA Enhancement Methodology Using Situational Parameter Adjustments

Abstract

In the context of close ground combat, the perception of Battle Damage Assessment (BDA) is closely linked with a soldier?s engagement decisions and has significant effects on the battlefield. Perceived BDA is also one of the most complex and uncertain processes facing the soldier in live combat. As a result, the modeling and simulation community has yet to adequately model the perceived BDA process in combat models. This research effort examines the BDA process from a perception standpoint and proposes a methodology to collect the pertinent data and model this perception in the Army?s current force-on-force model, CASTFOREM. A subject matter expert survey design and a method to model the BDA process as a Discrete Time Markov Chain are proposed. Bayesian inference is used to update probability distributions at each time step considering the Situational Parameters available to the soldier at the time of an assessment. Comparisons between known simulation distributions and those developed from simulated survey responses suggest an adequate number of subject matter experts to be polled.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2006
Accession Number
ADA449614

Entities

People

  • Michael V. Carras Jr.

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Ground and Sea Platforms
  • Human Systems
  • Weapons Technologies

DTIC Thesaurus Topics

  • Air Force
  • Armored Personnel Carriers
  • Battle Damage Assessment
  • Bayes Theorem
  • Bayesian Inference
  • Damage Assessment
  • Department Of Defense
  • Markov Chains
  • Mathematical Models
  • Military Operations
  • Probabilistic Models
  • Probability Distributions
  • Random Variables
  • Stochastic Processes
  • Surveys
  • United States
  • Warfare

Readers

  • Military Training and Readiness Simulation
  • Systems Analysis and Design
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.

Technology Areas

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