Cognitive Electronic Warfare : An Artificial Intelligence Approach.

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This comprehensive book gives an overview of how cognitive systems and artificial intelligence (AI) can be used in electronic warfare (EW). Readers will learn how EW systems respond more quickly and effectively to battlefield conditions where sophisticated radars and spectrum congestion put a high p...

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Bibliographic Details
Call Number:Libro Electrónico
Main Author: Haigh, Karen
Other Authors: Andrusenko, Julia
Format: Electronic eBook
Language:Inglés
Published: Norwood : Artech House, 2021.
Subjects:
Information warfare.
Guerre de l'information.
Information warfare
Online Access:Texto completo
Table of Contents:
  • Intro
  • Cognitive Electronic Warfare: An Artificial Intelligence Approach
  • Contents
  • Foreword
  • Preface
  • 1 Introduction to Cognitive EW
  • 1.1 What Makes a Cognitive System?
  • 1.2 A Brief Introduction to EW
  • 1.3 EW Domain Challenges Viewed from an AI Perspective
  • 1.3.1 SA for ES and EW BDA
  • 1.3.2 DM for EA, EP, and EBM
  • 1.3.3 User Requirements
  • 1.3.4 Connection between CR and EW Systems
  • 1.3.5 EW System Design Questions
  • 1.4 Choices: AI or Traditional?
  • 1.5 Reader's Guide
  • 1.6 Conclusion
  • References
  • 2 Objective Function
  • 2.1 Observables That Describe the Environment
  • 2.1.1 Clustering Environments
  • 2.2 Control Parameters to Change Behavior
  • 2.3 Metrics to Evaluate Performance
  • 2.4 Creating a Utility Function
  • 2.5 Utility Function Design Considerations
  • 2.6 Conclusion
  • References
  • 3 ML Primer
  • 3.1 Common ML Algorithms
  • 3.1.1 SVMs
  • 3.1.2 ANNs
  • 3.2 Ensemble Methods
  • 3.3 Hybrid ML
  • 3.4 Open-Set Classification
  • 3.5 Generalization and Meta-learning
  • 3.6 Algorithmic Trade-Offs
  • 3.7 Conclusion
  • References
  • 4 Electronic Support
  • 4.1 Emitter Classification and Characterization
  • 4.1.1 Feature Engineering and Behavior Characterization
  • 4.1.2 Waveform Classification
  • 4.1.3 SEI
  • 4.2 Performance Estimation
  • 4.3 Multi-Intelligence Data Fusion
  • 4.3.1 Data Fusion Approaches
  • 4.3.2 Example: 5G Multi-INT Data Fusion for Localization
  • 4.3.3 Distributed-Data Fusion
  • 4.4 Anomaly Detection
  • 4.5 Causal Relationships
  • 4.6 Intent Recognition
  • 4.6.1 Automatic Target Recognition and Tracking
  • 4.7 Conclusion
  • References
  • 5 EP and EA
  • 5.1 Optimization
  • 5.1.1 Multi-Objective Optimization
  • 5.1.2 Searching Through the Performance Landscape
  • 5.1.3 Optimization Metalearning
  • 5.2 Scheduling
  • 5.3 Anytime Algorithms
  • 5.4 Distributed Optimization
  • 5.5 Conclusion.

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