Projecting COSAGE Output in Discrete Time

Abstract

The Army's Combat Sample Generator (COSAGE) is a two-sided, symmetrical, high-resolution stochastic simulation model that projects the outcome of ground combat between two forces. Blue force is typically a division; Red force size may be scaled from a fraction of a division to a combined arms army. Because COSAGE is high-resolution (many asset types), it requires extensive data preparation time, and because output is the result of 16-20 replications, substantial simulation run-time. The analytical model implementation of this thesis is developed to economically project ground combat attrition and munitions expenditures beyond the 48-hour period currently modeled in COSAGE. The implementation evaluates Bayesian estimators of time-period survivorship to estimate expected numbers of kills, both friendly and enemy, during the first 48 hours of combat, then extrapolates those estimates in discrete time StepS (here 24 hours) beyond 48 hours. The implementation can be used to project COSAGE output for all combat postures in Northeast and Southwest Asia (NEA and SWA respectively). An application of the current implementation is to support the warfighting Commanders in Chief (CinC) need to create a Phased Threat Distribution (PTD) in accordance with the Capabilties-Based Munition Requirement Process introduced in June 1997.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1999
Accession Number
ADA381212

Entities

People

  • Marc C. Schweighofer

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Human Systems
  • Weapons Technologies

DTIC Thesaurus Topics

  • Air Force
  • Ammunition
  • Attrition
  • Basic Programming Language
  • Estimators
  • Firing Rate
  • High Resolution
  • Indirect Fire
  • Multiple Launch Rocket System
  • Munitions
  • Operations Research
  • Simulations
  • Southwest Asia
  • Spreadsheet Software
  • United States Naval Academy
  • Warfare
  • Weapons

Readers

  • Aerospace logistics and air mobility.
  • Computational Modeling and Simulation
  • Geochemistry

Technology Areas

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