Improving Air Battle Management Target Assignment Processes via Approximate Dynamic Programming

Abstract

Military air battle managers face many challenges when directing operations in quickly evolving combat scenarios. These scenarios require rapid decisions to engage moving and unpredictable targets. In defensive operations, the success of a sequence of air battle management decisions is reflected by the friendly force's ability to maintain air superiority by defending friendly assets. We develop a Markov decision process (MDP) model of the air battle management (ABM)problem, wherein a set of unmanned combat aerial vehicles (UCAV) is tasked to defend a central asset from cruise missiles that arrive stochastically over time.

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Document Details

Document Type
Technical Report
Publication Date
Mar 25, 2021
Accession Number
AD1131215

Entities

People

  • Joseph M. Iv Liles

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Air Platforms
  • Weapons Technologies

DTIC Thesaurus Topics

  • Air Defense
  • Air Force
  • Air Power
  • Aircrafts
  • Algorithms
  • Battle Management
  • Computational Science
  • Computer Programming
  • Control Systems
  • Cruise Missiles
  • Department Of Defense
  • Dynamic Programming
  • Equations
  • Governments
  • Law
  • Mathematical Models
  • Military Organizations
  • Neural Networks
  • Operations Research
  • Optimization
  • Probability
  • Simulations
  • United States
  • United States Government
  • Unmanned Aerial Systems
  • Unmanned Aerial Vehicles
  • Warfare

Fields of Study

  • Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Irregular Warfare and Special Operations Cyberspace Operations against Adversarial Threats.
  • Naval Mine Countermeasure Systems Development.

Technology Areas

  • Autonomy
  • Autonomy - UAVs