Cognitive Electronic Warfare: An Artificial Intelligence Approach by Karen Zita Haigh, Julia Andrusenko 9781630818128 1630818127

Cognitive Electronic Warfare: An Artificial Intelligence Approach Haigh – Ebook Instant Download/Delivery ISBN(s): 9781630818111,1630818119, 9781630818128, 1630818127

Product details:

• ISBN 10:1630818127
• ISBN 13:9781630818128
• Author:Karen Zita Haigh, Julia Andrusenko

Cognitive Electronic Warfare: An Artificial Intelligence Approach

Table contents:

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

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