General Markov Modeling of Pop-Up Threats with Applications to Persistent Area Denial

Abstract

Pop-up threats usually appear or disappear randomly in a battlefield. If the next pop-up threat locations could be predicted, it would assist a search or attack team in getting a team of unmanned air vehicles (UAVs) to the threats sooner, such as in the case of a Persistent Area Denial (PAD) mission. The authors present a Markov model for predicting pop-up ground threats in military operations. They first introduce a general Markov chain of order "n" to capture the dependence of the appearance of pop-up threats on previous locations of the pop-up threats over time. Then they present an adaptive approach to estimating the stationary transition probabilities of the "nth" order Markov models. To choose the order of the Markov chain model for a specific application, they also discuss hypothesis tests from statistical inference on historical data of pop-up threat locations. Anticipating intelligent responses from an adversary, which might change its pop-up threat deployment strategy upon observing UAV movements, the authors present adaptive Markov chain models using a moving horizon approach to estimate possibly abrupt changes in transition probabilities. They consider the problem of cooperative control among multiple networked UAVs for the PAD mission. The combined information of predicted and actual pop-up target locations is utilized to develop efficient cooperative strategies for networked UAVs. Both a theoretical analysis and simulation results are presented to evaluate the Markov model used for predicting pop-up threats. These results demonstrate the effectiveness of cooperative strategies using the combined information of threats and predicted threats in improving overall mission performance. Index terms: Pop-up threats; Markov chain model; Model order test; Cooperative strategies.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2006
Accession Number
ADA445061

Entities

People

  • Corey J. Schumacher
  • Jose B. Cruz Jr.
  • Yong Liu

Organizations

  • Ohio State University

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Air Force Research Laboratories
  • Aircrafts
  • Algorithms
  • Area Denial
  • Control Systems
  • Cooperative Control
  • Databases
  • Markov Chains
  • Markov Models
  • Military Applications
  • Military Operations
  • Probability
  • Random Variables
  • Simulations
  • Stochastic Processes
  • Test Methods
  • Unmanned Aerial Vehicles

Readers

  • Aerial Unmanned Vehicle Swarm Micro Periodontal Dentistry.
  • Mathematical Modeling and Probability Theory.
  • Sensor Fusion and Tracking Systems.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Bayesian Inference
  • Autonomy
  • Autonomy - Autonomous System Control
  • Autonomy - UAVs