Cognitive Electronic Warfare : : An Artificial Intelligence Approach.

• Author: Haigh, Karen.
• TeilnehmendeR: Andrusenko, Julia.
• Place / Publishing House: Norwood : : Artech House,, 2021.; ©2021.
• Year of Publication: 2021
• Edition: 1st ed.
• Language: English
• Physical Description: 1 online resource (261 pages)

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.
• References
• 6 EBM
• 6.1 Planning
• 6.1.1 Planning Basics: Problem Definition, and Search
• 6.1.2 Hierarchical Task Networks
• 6.1.3 Action Uncertainty
• 6.1.4 Information Uncertainty
• 6.1.5 Temporal Planning and Resource Management
• 6.1.6 Multiple Timescales
• 6.2 Game Theory
• 6.3 HMI
• 6.4 Conclusion
• References
• 7 Real-Time In-mission Planning and Learning
• 7.1 Execution Monitoring
• 7.1.1 EW BDA
• 7.2 In-Mission Replanning
• 7.3 In-Mission Learning
• 7.3.1 Cognitive Architectures
• 7.3.2 Neural Networks
• 7.3.3 SVMs
• 7.3.4 Multiarmed Bandi
• 7.3.5 MDPs
• 7.3.6 Deep Q-Learning
• 7.4 Conclusion
• References
• 8 Data Management
• 8.1 Data Management Process
• 8.1.1 Metadata
• 8.1.2 Semantics
• 8.1.3 Traceability
• 8.2 Curation and Bias
• 8.3 Data Management
• 8.3.1 Data in an Embedded System
• 8.3.2 Data Diversity
• 8.3.3 Data Augmentation
• 8.3.4 Forgetting
• 8.3.5 Data Security
• 8.4 Conclusion
• References
• 9 Architecture
• 9.1 Software Architecture: Interprocess
• 9.2 Software Architecture: Intraprocess
• 9.3 Hardware Choices
• 9.4 Conclusion
• References
• 10 Test and Evaluation
• 10.1 Scenario Driver
• 10.2 Ablation Testing
• 10.3 Computing Accuracy
• 10.3.1 Regression and Normalized RMSE
• 10.3.2 Classification and Confusion Matrices
• 10.3.3 Evaluating Strategy Performance
• 10.4 Learning Assurance: Evaluating a Cognitive System
• 10.4.1 Learning Assurance Process
• 10.4.2 Formal Verification Methods
• 10.4.3 Empirical and Semiformal Verification Methods
• 10.5 Conclusion
• References
• 11 Getting Started: First Steps
• 11.1 Development Considerations
• 11.2 Tools and Data
• 11.2.1 ML Toolkits
• 11.2.2 ML Datasets
• 11.2.3 RF Data-Generation Tools
• 11.3 Conclusion
• References
• Acronyms
• About the Authors
• Index.