
Essential Principles of Signals Collection and Analysis
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Content
- Intro
- Essential Principles of Signals Collection and Analysis
- Contents
- Foreword
- 1 Introduction to Signals Collection and Analysis
- 1.1 Key Foundations
- 1.2 Presentation Organization
- 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 Wi-Fi
- 2.8 RF Signal Detection
- 2.9 Conclusions
- References
- 4 Radar Fundamentals
- 4.1 Introduction
- 4.2 Electromagnetic Theory
- 4.2.1 Maxwell's Equations
- 4.2.2 EM Wave Propagation
- 4.2.3 EM Wave Polarization
- 4.3 Antennas
- 4.3.1 Pure Magnetic Dipole Antenna Concept
- 4.3.2 Transmission and Reception Beam Patterns
- 4.4 Radar Equation
- 4.4.1 Signal Power Over One-Way Link
- 4.4.2 Target Radar Cross Section
- 4.4.3 EM Power Density for Monostatic Receiver
- 4.5 Summary
- References
- 5 Geolocation
- 5.1 Introduction
- 5.1.1 Emitter and Collector Assumptions
- 5.1.2 Possible Vignettes
- 5.2 Angle of Arrival Methods
- 5.2.1 Array Model for AoA/DOA Estimation
- 5.2.2 Array Transfer Vector
- 5.2.3 Uniform Linear Array for AoA/DoA Estimation
- 5.3 Time Difference of Arrival Methods
- 5.3.1 Time of Arrival
- 5.3.2 Difference of Time of Arrivals
- 5.3.3 Hyperbolic Isochrones
- 5.3.4 TDOA Calculation Details
- 5.4 Frequency Difference of Arrival Methods
- 5.4.1 Doppler Frequency
- 5.4.2 Difference of Doppler Frequencies
- 5.4.3 FDOA Calculation Details
- 5.5 Cross Ambiguity Function
- 5.6 Summary
- References
- 3 Access to RF Signals
- 3.1 Introduction
- 3.2 RF Signal Collection Platforms
- 3.2.1 RF Signal Collection from Ground
- 3.2.2 RF Signal Collection from Water
- 3.2.3 RF Signal Collection from Air
- 3.2.4 RF Signal Collection from Space
- 3.3 Altitude and Shape of Orbits
- 3.3.1 Low-Earth Orbit
- 3.3.2 Medium-Earth Orbit
- 3.3.3 High-Earth Orbit
- 3.3.4 Special Orbit Terminology
- 3.4 Orbital Mechanics
- 3.5 Conclusion
- References
- 6 Signals Detection and Estimation
- 6.1 Introduction
- 6.2 Detection Theory
- 6.2.1 Signal Detection Terminology
- 6.2.2 Signal Detection Strategy
- 6.3 Detection of Signals in Noise
- 6.3.1 Matched Filter
- 6.3.2 Matched Filter: Correlator and Convolution
- 6.3.3 White Noise Versus Colored Noise
- 6.3.4 Detection of Known Signals in White Noise
- 6.3.5 Detection of Known Signals in Colored Noise
- 6.3.6 Detection of Random Signals
- 6.3.7 Detection in Non-Gaussian Noise
- 6.3.8 Designing Detectors with Unknown Parameters
- 6.4 Signal Parameter Estimation
- 6.4.1 Minimum Variance Unbiased Estimation
- 6.4.2 Cramer-Rao Lower Bound
- 6.4.3 Maximum Likelihood Estimation
- 6.4.4 Maximum A Posteriori Estimator
- 6.5 Summary
- References
- 7 Cryptography Fundamentals
- 7.1 Introduction
- 7.1.1 Connection with Signals Collection
- 7.1.2 Primary Goals of Cryptography
- 7.1.3 Primary Algorithm Types
- 7.2 Mathematical Fundamentals of Cryptography
- 7.2.1 Modulo Arithmetic
- 7.2.2 Euler's Totient Function
- 7.3 Symmetric Cryptography
- 7.3.1 Model of Symmetric Cryptography
- 7.3.2 Key Generation and Distribution
- 7.3.3 Stream Ciphers
- 7.3.4 Block Ciphers
- 7.4 Asymmetric Cryptography
- 7.4.1 Basic Concepts
- 7.4.2 RSA Algorithm
- 7.5 Summary
- References
- 8 Signal Networks
- 8.1 Introduction
- 8.1.1 Wired Signals
- 8.1.2 Wireless Signals
- 8.2 Protocol Stack
- 8.2.1 Physical Layer
- 8.2.2 Data Link Layer
- 8.2.3 Network Layer
- 8.2.4 Transport Layer
- 8.2.5 Application Layer
- 8.3 Types of Message Delay
- 8.3.1 Propagation Delay
- 8.3.2 Transmission Delay
- 8.3.3 Router Processing Delay
- 8.3.4 Queuing Delay
- 8.4 Summary
- References
- 9 Machine Learning for RF Signal Classification
- 9.1 Introduction
- 9.2 RF Signal Classification Problems
- 9.2.1 RF Feature Learning
- 9.2.2 Attention and Saliency
- 9.2.3 Autonomous RF Sensor Configurations
- 9.2.4 Waveform Synthesis
- 9.3 An Overview of Machine Learning Algorithms
- 9.4 Deep Neural Networks
- Convolution Layers
- Pooling Layers
- Fully Connected Layers
- DropOut Layer
- 9.5 RF Signal Dataset for Wi-Fi Fingerprinting
- 9.6 Deep-Learning-Based Wi-Fi Signal Classification
- RF Signal Data Preparation
- Deep-Learning Model Preparation
- Updated Deep-Learning Model and Training
- Results
- 9.7 Conclusion
- References
- 10 Future Trends in Signals Collection and Analysis
- 10.1 Introduction
- 10.2 Unmanned Sensors
- 10.3 Higher Frequency Communication Networks: 6G and Beyond
- 10.4 Internet of Things
- 10.5 Enhanced AI/ML Applications
- 10.5.1 Robust and Reliable Classifications for Noisy Signals
- 10.5.2 Signals Classification Algorithm's Performance Evaluations
- 10.6 Conclusion
- References
- About the Authors
- Index
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