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.