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Artech House UK
Cognitive Electronic Warfare: An Artificial Intelligence Approach

Cognitive Electronic Warfare: An Artificial Intelligence Approach

Copyright: 2021
Pages: 288
ISBN: 9781630818111

Artech House is pleased to offer you this title in a special In-Print-Forever® ( IPF® ) hardbound edition. This book is not available from inventory but can be printed at your request and delivered within 2-4 weeks of receipt of order. Please note that because IPF® books are printed on demand, returns cannot be accepted.

Print Book £94.00 Qty:
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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 priority on EW systems that can characterize and classify novel waveforms, discern intent, and devise and test countermeasures. Specific techniques are covered for optimizing a cognitive EW system as well as evaluating its ability to learn new information in real time.


The book presents AI for electronic support (ES), including characterization, classification, patterns of life, and intent recognition. Optimization techniques, including temporal tradeoffs and distributed optimization challenges are also discussed. The issues concerning real-time in-mission machine learning and suggests some approaches to address this important challenge are presented and described. The book covers electronic battle management, data management, and knowledge sharing. Evaluation approaches, including how to show that a machine learning system can learn how to handle novel environments, are also discussed. Written by experts with first-hand experience in AI-based EW, this is the first book on in-mission real-time learning and optimization.

Intro to Cognitive EW; Objective Function; Machine Learning Primer; Electronic Support; Electronic Protect and Electronic Attack; Electronic Battle Management; Real-time In-mission Planning and Learning; Data Management; Architecture; Test and Evaluation.
  • Karen Zita Haigh

    is a Chief Fellow Technologist (Artificial Intelligence) at Mercury Systems. She received her Ph.D. in computer science from Carnegie Mellon University. She holds six patents, has authored dozens of publications and is a member of IEEE.

  • Julia Andrusenko

    is a chief engineer at the Johns Hopkins University - Applied Physics Laboratory. She received her M.S. in electrical engineering from Drexel University. She has authored several publications and is a member of IEEE.

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