A Continuous Stochastic Model for Penetration Problems

Abstract

This report is concerned with the problem of estimating the survival of elements of a penetrating attack force using a variety of penetration aids. This problem has a long history, having been investigated by a number of aircraft, research, and government organizations. Most approaches to the problem are based on the fact that the status of the penetrating force depends on a time sequence of events with the property that each event depends upon the history of previous events, all actions that have been taken, and random outcomes associated with these events and actions. The level of detail in the modeling usually dictates the proper approach to the problem. Simple formulations may be handled using elementary methods of probability. Complex versions of the problem require either Monte Carlo or Markov Chain solutions. Both Monte Carlo and Markov Chain approaches keep track of changes in the state of the penetration process as discrete time events and random outcomes based on these events and the current state of the process occur. The Markov Chain solution actually requires discrete time updating of the probability distribution defined on the state space. There are certain size state space problems for which the Markov Chain approach is preferred over the Monte Carlo approach.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1976
Accession Number
ADB013459

Entities

People

  • Frank C. Reed

Organizations

  • Naval Air Weapons Station China Lake

Tags

Communities of Interest

  • Weapons Technologies

DTIC Thesaurus Topics

  • Air Force
  • Attrition
  • Defense Suppression
  • Differential Equations
  • Electronic Countermeasures
  • Equations
  • Governments
  • Jet Propulsion
  • Markov Chains
  • Military Research
  • Noise Jamming
  • Penetration Aids
  • Probability
  • Probability Distributions
  • Stochastic Processes
  • Test And Evaluation
  • War Colleges

Readers

  • Computational Modeling and Simulation
  • Mathematical Modeling and Probability Theory.

Technology Areas

  • Space