An Optimization Framework for Intelligence, Surveillance, and Reconnaissance Systems

Abstract

This project is to investigate and verify the feasibility for development of a methodological approach and corresponding tools for the management of intelligence, surveillance, and reconnaissance (ISR) systems. Our focus is on problem classes for which fast heuristics may be developed for both the construction of feasible solutions and for the improvement of such solutions. However, rather than considering heuristics in isolation, we wish to obtain maximum benefit from their availability by employing them within partition-based strategies. This research is built on the very recent research in the area of computational intelligence. The newly developed methodology, the Nested Partitions (NP) framework has its ability to incorporate feasibility heuristics (in which a number of good quality feasible solutions are generated via problem-specific techniques) as well as general search heuristics such as Tabu Search (TS), Greedy Search (GS), and Genetic Algorithms (GA's).

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 2007
Accession Number
ADA473334

Entities

People

  • Leyuan Shi

Organizations

  • University of New Mexico

Tags

Communities of Interest

  • C4I

DTIC Thesaurus Topics

  • Algorithms
  • Applied Mathematics
  • Artificial Intelligence
  • Computational Complexity
  • Dynamic Programming
  • Engineering
  • Evolutionary Algorithms
  • Genetic Algorithms
  • Information Systems
  • Linear Programming
  • Mathematics
  • Operations Research
  • Optimization
  • Reconnaissance
  • Simplex Method
  • Surveillance
  • Systems Engineering

Fields of Study

  • Computer science

Readers

  • Aerospace Test and Evaluation
  • Distributed Systems and Data Platform Development
  • Operations Research

Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy
  • AI & ML - Machine Learning Algorithms
  • Biotechnology