Description
In this revised Second edition of Cognitive Electronic Warfare: An Artificial Intelligence Approach, you will learn how cutting-edge AI and machine learning technologies are transforming the landscape of national defense. This comprehensive guide demystifies how cognitive systems are being used to revolutionize Electronic Warfare (EW), from real-time signal analysis to autonomous decision-making. Whether you're a defense analyst, engineer, or tech innovator, this book offers a foundational understanding of how AI can be applied to the full spectrum of EW operations—from support and protection to electronic attack and battle management.
Modern battlefields demand systems that are fast, flexible, and capable of adapting without direct human intervention. This book goes beyond the basics to show you how to architect intelligent EW platforms that can learn and evolve during missions, even when deployed in resource-constrained environments. You'll explore how machine learning can be used for signal characterization, classification, pattern recognition, and intent detection—key functions that enhance situational awareness and response. The text also dives into the specifics of planning and optimization techniques for real-time electronic protect (EP) and electronic attack (EA), ensuring you understand the spatial and temporal tradeoffs inherent in these systems. With hands-on case studies, development strategies, and insight into data and knowledge management, this book serves as a technical and strategic blueprint for building next-generation defense systems that are as adaptive as they are effective.
Cognitive Electronic Warfare: An Artificial Intelligence Approach, Second Edition stands alone in its comprehensive treatment of EW capabilities across all domains and timescales. Thoroughly updated to reflect rapid advancements in technology and operational needs, it introduces cutting-edge topics such as on-board learning, generative AI, tiny ML, and DevMLOps—equipping readers to meet the demands of modern, agile system development. Most importantly, the book addresses one of the most critical aspects of AI in defense: evaluation and assurance. You'll learn robust techniques for validating cognitive systems in complex, unpredictable environments, with a focus on building trust, reliability, and mission readiness. Whether you're designing systems for the lab or the tactical edge, this book gives you the tools to deploy AI that works—and proves it.
Table Of Contents
1 Introduction to Cognitive EW
1.1 The Vision of Cognitive EW
1.2 What is a Cognitive System?
1.3 A Brief Introduction to AI
1.4 A Brief Introduction to Electronic Warfare
1.5 Cognition in EW
1.6 Civilian Electronic Warfare
1.7 Cognitive Radio, Cognitive Radar, and Cognitive EW
1.8 EW System Design Questions
1.9 EW Domain Challenges Viewed from an AI perspective
1.10 Reader’s Guide
1.11 Conclusion
2 Objective Function
2.1 Observables that Describe the Environment
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 Example Observables, Controllables, and Metrics
2.7 Conclusion
3 Machine Learning Primer
3.1 Introduction to ML
3.2 Common ML Algorithms
3.3 Generalization to Surprise
3.4 Hybrid ML
3.5 Open-Set Classification
3.6 Metalearning
3.7 Generative AI
3.8 Embedded ML
3.9 Training an ML model
3.10 Algorithmic Trade-Offs
3.11 The Speed of AI
3.12 Conclusion
4 Electronic Support
4.1 Detection, Localization, and Signal Separation
4.2 Emitter Classification and Characterization
4.3 Performance Estimation
4.4 Multisensor Data Fusion
4.5 Anomaly Detection
4.6 Causal Relationships
4.7 Intent Recognition
4.8 Conclusion
5 Electronic Protect and Electronic Attack
5.1 Optimization
5.2 Scheduling
5.3 Reward Hacking
5.4 Anytime Algorithms
5.5 Centralized, Distributed, and Decentralized Optimization
5.6 Conclusion
6 Electronic Battle Management
6.1 Planning
6.2 Game Theory
6.3 Human-Machine Interface
6.4 Conclusion
7 Real-Time In-Mission Planning and Learning
7.1 Execution Monitoring
7.2 In-Mission Replanning
7.3 In-Mission Learning
7.4 Conclusion
8 Data Management
8.1 Data Quality Control
8.2 Data Modeling: Ontologies, Metadata, and Schemas
8.3 Data Management Practice
8.4 Conclusion
9 Architecture
9.1 Software Architecture: Interprocess
9.2 Software Architecture: Intraprocess
9.3 Language Choices
9.4 Hardware Choices
9.5 Conclusion
10 Test & Evaluation
10.1 Paradigm Shifts
10.2 Validating the Learning Process
10.3 Evaluate Learning Goals
10.4 Determine Range of Operational Effectiveness
10.5 Mixed-Fidelity Closed-Loop Testing
10.6 Behavior-based Models with Closed-loop Effects
10.7 Smart Experimental Design
10.8 Computing Accuracy and Adequacy
10.9 Ablation Testing
10.10 Verification Approaches
10.11 Example Implementation: Scenario Driver
10.12 Conclusion
11 Getting Started: First Steps
11.1 Engineering Resilient Systems
11.2 Development Considerations
11.3 Choices: AI or Traditional?
11.4 ML Toolkits
11.5 RF Datasets and RF Data-Generation Tools
11.6 Projects
11.7 Conclusion
Acronyms
About the Authors
Index