Deception Algorithm

Abstract

Combat modeling has shown that close combat decoys can improve Loss Exchange Ratios by 20-50% and that models that do not realistically address the effects of close combat decoys understate Loss Exchange Ratios by 15-25% when close combat decoys are employed. This report describes an algorithm that was created to realistically address the effects and operational impacts of deception and close combat decoys in standard Army wargames and analytical models such as Janus, CASTFOREM, and the Semi-Automated forces model in SIMNET. Data for the algorithm were obtained from live force-on-force exercises conducted in conjunction with the Multispectral Close Combat Decoy (MCCD) initial Operational Test and Evaluation (IOTE) conducted at Fort Hunter Liggett during the Spring of 1993. These data reflect the operational impact of the most commonly recognized effects of decoys which includes the detection, identification and engagement of the decoys themselves, and the misidentification and engagement of false targets and non targets by opposing forces.

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Document Details

Document Type
Technical Report
Publication Date
Jun 30, 1994
Accession Number
ADA281077

Tags

Communities of Interest

  • Human Systems
  • Materials and Manufacturing Processes
  • Sensors
  • Weapons Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Computational Science
  • Computer Simulations
  • Computers
  • Data Analysis
  • Databases
  • Detection
  • False Targets
  • Infantry Fighting Vehicles
  • Information Science
  • Probability
  • Simulations
  • Static Tests
  • Statistical Analysis
  • Target Acquisition
  • Test And Evaluation
  • Warfare

Readers

  • Computational Modeling and Simulation
  • Military Science
  • Sensor Fusion and Tracking Systems.

Technology Areas

  • 5G
  • 5G - DoD 5G Program