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Artech House USA
Essential Principles of Signals Collection and Analysis

Essential Principles of Signals Collection and Analysis

Copyright: 2023
Pages: 300
ISBN: 9781630816919

Digital download and online $118.00 Qty:

Chapter 1: Introduction to Signals Collection and Processing
1 Introduction to Signals Collection and Processing
1.1 Introduction
1.2 Presentation Organization

 

Chapter 2: RF Signals Classification
2.1 Introduction
2.2 Communication Channels
2.3 RF Signals Analysis
2.4 RF Analog Signals Modulation
2.5 RF Digital Signals Modulation
2.6 RF Shift Keying
2.7 RF WiFi
2.8 Signal Detection
2.9 Conclusion

 

Chapter 3: Access to RF Signals
3.1 Introduction
3.2 RF Signal Collection Platforms
3.3 Altitude and Shape of Orbits
3.4 Orbital Mechanics
3.5 Conclusion

 

Chapter 4: Radar Fundamentals
4.1 Introduction
4.2 Electro-Magnetic (EM) Theory
4.3 Antennas
4.4 Radar Equation
4.5 Summary

 

Chapter 5: Geolocation
5.1 Introduction
5.2 Angle of Arrival Methods
5.3 Time Difference of Arrival (TDOA) Methods
5.4 Frequency Difference of Arrival (FDOA) Methods
5.5 Cross Ambiguity Function (CAF)
5.6 Summary

 

Chapter 6: Signals Detection and Estimation
6.1 Introduction
6.2 Detection Theory
6.3 Detection of Signals in Noise
6.4 Signal Parameter Estimation
6.5 Summary

 

Chapter 7: Cryptography Fundamentals
7.1 Introduction
7.2 Mathematical Fundamentals of Cryptography
7.3 Symmetric Cryptography
7.4 Asymmetric Cryptography
7.5 Summary

 

Chapter 8: Signal Networks
8.1 Introduction
8.2 Internet Protocol Stack
8.3 Types of Packet Delay
8.4 Summary

 

Chapter 9: Machine Learning for RF Signal Classification
9.1 Introduction
9.3 An Overview of Machine Learning Algorithms
9.4 Deep Neural Networks
9.5 RF Signal Dataset for WiFi Fingerprinting
9.6 Deep Learning Based WiFi Signal Classification
9.7 Conclusion

 

Chapter 10: Future Trends in Signals Collection and Analysis
10.1 Introduction
10.2 Unmanned Sensors
10.3 Higher Frequency Communication Networks (e.g., 6-G and Beyond)
10.4 Internet of Things (IoT)
10.5 Enhanced AI/ML Applications
10.6 Conclusion

  • Erik P. Blasch

    is a program officer at the United States Air Force Research Laboratory (AFRL) Air Force Office of Scientific Research (AFOSR). He received Ph.D. in electrical engineering from Wright State University. He is a Fellow of IEEE.

  • Uttam K. Majumder is a senior electronics engineer at the United States Air Force Research Laboratory (AFRL). He received his Ph.D. in electrical engineering from Purdue University. He is a senior member of IEEE.
  • David A. Garren

    is a professor at the Naval Postgraduate School. He received his Ph.D. from the College of William and Mary. He is a senior member of IEEE.

© 2024 Artech House