Enhancing the Resource Efficiency of Live-Fire Tank Gunnery Evaluation.

Abstract

This investigation reports the development of a target engagement reduction methodology that supports resource-efficient, live-fire gunnery evaluation on Tank Table VIII (TTVIII), the intermediate-level tank crew gunnery certification exercise. Through a series of multiple regression analyses, it was determined that TTVIII can be reduced from its current 16 engagements to 7 engagements. Scores on these 7 engagements can be used to predict 10-engagement-based TTVIII total scores with greater than 85% predictive accuracy. For Army National Guard (ARNG) units, the 7 engagements can be selected randomly. For Active Component (AC) units, however, the predictive subset must consist of specific engagements. For the ARNG, subsets consisting of as few as two engagements can be used to identify tank crews with little chance of achieving first-run qualification (Q1), and subsets consisting of as few as four engagements can be used to identify crews with a high probability of firing Q1. Both predictions can be made with 95% accuracy. For both the ARNG and AC, short-cut scoring models allowed the prediction of 10-engagement-based TTVIII total scores, based on subsets of any size, with calculational ease. It was concluded that more resource-efficient live-fire tank gunnery evaluation is possible in both the ARNG and AC without sacrificing evaluative validity. The magnitude of resource savings to be anticipated from use of the recommended resource-efficient methods was estimated.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1998
Accession Number
ADA368641

Entities

People

  • Joseph D. Hagman
  • Monte D. Smith

Tags

Communities of Interest

  • Energy and Power Technologies
  • Human Systems
  • Weapons Technologies

DTIC Thesaurus Topics

  • Accuracy
  • Army Personnel
  • Data Science
  • Data Sets
  • Databases
  • Efficiency
  • Information Science
  • Military Research
  • National Guard
  • Predictive Modeling
  • Probability
  • Qualifications
  • Regression Analysis
  • Social Sciences
  • Statistics
  • Test And Evaluation
  • Training

Readers

  • Computational Modeling and Simulation
  • Marksmanship and Weaponry.