Interceptor-to-Target Allocation Strategies for Strategic Defense 1: adaptive Strategies Based Upon System and Threat Characteristics

Abstract

This paper examines the topic of interceptor-to-target assignment algorithms and compares the system performance of a strategic defense system (SDS) as a function of the algorithm used. The algorithms used in this study range from being similar to some of those being developed under the SDIO's advanced algorithm programs to those used in some of the more popular engagement models. It is shown that the use of less sophisticated algorithms can often lead to RV leakages twice as large compared to cases in which sophisticated algorithms are used. The primary conclusion from this paper is that as the defense becomes interceptor-rich and/or the Pk of the interceptor becomes low, the popular one-interceptor-per-target approaches to interceptor allocation, which are typically found in the community-wide engagement models, can perform poorly. When the defense is in either, or both, of these regimes, it can perform significantly better by adopting an approach in which the salvo becomes a credible strategy. Because the salvo strategies have not been implemented in the popular engagement models, the past analyses which have studied these types of architectures have been biased toward providing a lower level of performance. Interceptor allocation strategies for the midcourse and terminal phase are not analyzed in this paper.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1988
Accession Number
ADA197037

Entities

People

  • Maile E. Smith
  • Thomas S. Paterson

Organizations

  • Institute for Defense Analyses

Tags

Communities of Interest

  • Weapons Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Availability
  • Battle Management
  • Boost Phase
  • Classification
  • Contracts
  • Defense Systems
  • Linear Programming
  • Probability
  • Resilience
  • Rules Of Engagement
  • Security
  • Simulations
  • Terminals
  • United States
  • Urban Areas
  • Weapons

Readers

  • Game Theory.
  • Missile Defense Systems.
  • Neural Network Machine Learning.