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Cognitive Electronic Warfare: An Artificial Intelligence Approach Unabridged edition [Kõva köide]

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  • Formaat: Hardback, 288 pages
  • Ilmumisaeg: 31-Jul-2021
  • Kirjastus: Artech House Publishers
  • ISBN-10: 1630818119
  • ISBN-13: 9781630818111
Teised raamatud teemal:
Cognitive Electronic Warfare: An Artificial Intelligence Approach Unabridged editionSuurem pilt
  • Formaat: Hardback, 288 pages
  • Ilmumisaeg: 31-Jul-2021
  • Kirjastus: Artech House Publishers
  • ISBN-10: 1630818119
  • ISBN-13: 9781630818111
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781630818111.html
Märksõnad:
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.
Foreword xi
Preface xiii
1 Introduction to Cognitive Electronic Warfare
1(20)
1.1 What Makes a Cognitive System?
2(2)
1.2 A Brief Introduction to EW
4(2)
1.3 EW Domain Challenges Viewed from an AI Perspective
6(7)
1.3.1 Situation-Assessment for Electronic Support and Electronic Warfare Battle Damage Assessment
7(1)
1.3.2 Decision-Making for Electronic Attack, Electronic Protect, and Electronic Battle Management
8(2)
1.3.3 User Requirements
10(1)
1.3.4 Connection between Cognitive Radio and EW Systems
11(1)
1.3.5 EW System Design Questions
12(1)
1.4 Choices: AI or Traditional?
13(3)
1.5 Reader's Guide
16(1)
1.6 Conclusion
17(4)
References
17(4)
2 Objective Function
21(20)
2.1 Observables That Describe the Environment
23(5)
2.1.1 Clustering Environments
24(4)
2.2 Control Parameters to Change Behavior
28(2)
2.3 Metrics to Evaluate Performance
30(2)
2.4 Creating a Utility Function
32(4)
2.5 Utility Function Design Considerations
36(2)
2.6 Conclusion
38(3)
References
38(3)
3 Machine Learning Primer
41(16)
3.1 Common ML Algorithms
43(3)
3.1.1 Support Vector Machines
43(1)
3.1.2 Artificial Neutral Networks
44(2)
3.2 Ensemble Methods
46(1)
3.3 Hybrid ML
47(1)
3.4 Open-Set Classification
48(1)
3.5 Generalization and Meta-learning
48(1)
3.6 Algorithmic Trade-Offs
49(3)
3.7 Conclusion
52(5)
References
52(5)
4 Electronic Support
57(28)
4.1 Emitter Classification and Characterization
58(5)
4.1.1 Feature Engineering and Behavior Characterization
59(1)
4.1.2 Waveform Classification
60(1)
4.1.3 Specific Emitter Identification
61(2)
4.2 Performance Estimation
63(2)
4.3 Multi-Intelligence Data Fusion
65(4)
4.3.1 Data Fusion Approaches
66(1)
4.3.2 Example: 5G Multi-INT Data Fusion for Localization
67(1)
4.3.3 Distributed-Data Fusion
68(1)
4.4 Anomaly Detection
69(2)
4.5 Causal Relationships
71(1)
4.6 Intent Recognition
72(3)
4.6.1 Automatic Target Recognition and Tracking
74(1)
4.7 Conclusion
75(10)
References
75(10)
5 Electronic Protect and Electronic Attack
85(26)
5.1 Optimization
87(9)
5.1.1 Multi-Objective Optimization
88(2)
5.1.2 Searching Through the Performance Landscape
90(4)
5.1.3 Optimization Metalearning
94(2)
5.2 Scheduling
96(3)
5.3 Anytime Algorithms
99(1)
5.4 Distributed Optimization
100(2)
5.5 Conclusion
102(9)
References
103(8)
6 Electronic Battle Management
111(28)
6.1 Planning
113(13)
6.1.1 Planning Basics: Problem Definition, and Search
115(1)
6.1.2 Hierarchical Task Networks
116(1)
6.1.3 Action Uncertainty
117(3)
6.1.4 Information Uncertainty
120(2)
6.1.5 Temporal Planning and Resource Management
122(3)
6.1.6 Multiple Timescales
125(1)
6.2 Game Theory
126(1)
6.3 Human-Machine Interface
127(5)
6.4 Conclusion
132(7)
References
132(7)
7 Real-Time In-Mission Planning and Learning
139(20)
7.1 Execution Monitoring
139(5)
7.1.1 Electronic Warfare Battle Damage Assessment
141(3)
7.2 In-Mission Replanning
144(4)
7.3 In-Mission Learning
148(5)
7.3.1 Cognitive Architectures
150(1)
7.3.2 Neural Networks
150(1)
7.3.3 Support Vector Machines
151(1)
7.3.4 Multiarmed Bandit
151(1)
7.3.5 Markov Decision Processes
152(1)
7.3.6 Deep Q-Learning
152(1)
7.4 Conclusion
153(6)
References
153(6)
8 Data Management
159(24)
8.1 Data Management Process
160(9)
8.1.1 Metadata
162(5)
8.1.2 Semantics
167(2)
8.1.3 Traceability
169(1)
8.2 Curation and Bias
169(2)
8.3 Data Management Practice
171(7)
8.3.1 Data in an Embedded System
172(1)
8.3.2 Data Diversity
172(2)
8.3.3 Data Augmentation
174(2)
8.3.4 Forgetting
176(1)
8.3.5 Data Security
177(1)
8.4 Conclusion
178(5)
References
179(4)
9 Architecture
183(10)
9.1 Software Architecture: Interprocess
183(4)
9.2 Software Architecture: Intraprocess
187(2)
9.3 Hardware Choices
189(2)
9.4 Conclusion
191(2)
References
191(2)
10 Test and Evaluation
193(28)
10.1 Scenario Driver
194(4)
10.2 Ablation Testing
198(2)
10.3 Computing Accuracy
200(9)
10.3.1 Regression and Normalized Root-Mean-Square Error
201(1)
10.3.2 Classification and Confusion Matrices
201(5)
10.3.3 Evaluating Strategy Performance
206(3)
10.4 Learning Assurance: Evaluating a Cognitive System
209(8)
10.4.1 Learning Assurance Process
209(5)
10.4.2 Formal Verification Methods
214(1)
10.4.3 Empirical and Semiformal Verification Methods
215(2)
10.5 Conclusion
217(4)
References
217(4)
11 Getting Started: First Steps
221(12)
11.1 Development Considerations
222(2)
11.2 Tools and Data
224(4)
11.2.1 ML Toolkits
224(3)
11.2.2 ML Datasets
227(1)
11.2.3 Radio Frequency Data-Generation Tools
227(1)
11.3 Conclusion
228(5)
References
228(5)
Acronyms 233(4)
About the Authors 237(2)
Index 239