Essential Principles of Signals Collection and Analysis
Published on 31. July 2023
300 pages
978-1-63081-691-9 (ISBN)
Description
This book provides a clear and systematic introduction to the basic concepts, foundational mathematics principles, and application of signals collection and supporting technologies. It describes the essential principles of signals collection and analysis for both tactical and commercial applications, and applies the foundational principles and concepts pertaining to the collection and exploitation of uncooperative signals of interest. You will be equipped with the basic concepts and skills necessary to manage and develop signals collection systems, and benefit from the practical, hands-on information you can confidently implement and apply. You will also find abundant references included to guide you further in the areas of signals collection. The book uniquely presents the fundamental mathematics of the collection and processing of signals in a manner easily understood by newcomers to the field, while practitioners will find it to be a practical, go-to desktop reference for signal analysis. This is an ideal, one-of-a-kind text for graduate students, analysts, managers, and others who need a rapid introduction to the diverse and growing field of signals analytics.
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David Garren | Uttam Majumder | Erik Blasch
Essential Principles of Signals Collection and Analysis
Book
07/2023
Artech House Publishers
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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
