<p>This two-volume book set explores how sensors and computer vision technologies are used for the navigation, control, stability, reliability, guidance, fault detection, self-maintenance, strategic re-planning and reconfiguration of unmanned aircraft systems (UAS).</p> <p>Volume 1 concentrates on U
Ground Penetrating Radar: Improving sensing and imaging through numerical modeling (Control, Robotics and Sensors)
โ Scribed by X. Lucas Travassos, Mario F. Pantoja, Nathan Ida
- Publisher
- Institution of Engineering and Technology
- Year
- 2021
- Tongue
- English
- Leaves
- 346
- Category
- Library
No coin nor oath required. For personal study only.
โฆ Synopsis
Ground Penetrating Radar (GPR) is a powerful sensing technology widely used for the non-destructive assessment of a variety of structures with different properties including dimensions, electrical properties, and moisture.
After an introduction to the underlying concepts, this book guides the reader through the development and use of a GPR system, with an emphasis on the parameters that can be optimized, the theory behind assessment, and a coherent methodology to obtain results from a measured or simulated GPR signal. The authors then embark on a detailed discussion of support tools and numerical modelling techniques that can be applied to improve readings from GPR systems.
Ground Penetrating Radar is of interest to engineers, scientists, researchers and professionals working in the fields of ground penetrating radar, non-destructive testing, geoscience and remote sensing, antennas and propagation, microwaves, electromagnetics and imaging. It will also be of use to professionals and academics in the fields of electrical, mechanical, sensing, and civil engineering as well as material science and archaeology concerned with quality control and fault analysis.
โฆ Table of Contents
Cover
Contents
About the authors
Preface
Acknowledgments
1 Introduction to ground penetrating radar
1.1 Introduction
1.2 Overview of a GPR system
1.3 Fundamental theory of GPR
1.3.1 Electromagnetic wave propagation
1.3.2 Material properties
1.3.3 Antennas
1.3.4 System specification
1.4 Post-processing support tools
1.4.1 Signal and image processing techniques
1.4.2 Pattern recognition
1.4.2.1 Principal component analysis
1.4.2.2 Discriminant analysis
1.4.2.3 Feature selection
1.4.2.4 Markov models
1.5 Summary
References
2 Electromagnetic wave propagation
2.1 Introduction
2.2 The electromagnetic wave equation and its solution
2.2.1 The time-dependent wave equation
2.2.2 The time-harmonic wave equations
2.2.3 The wave equation in lossy dielectrics
2.2.4 Solution of the wave equation
2.2.4.1 Solution for uniform plane waves
2.2.4.2 The one-dimensional wave equation in free-space and perfect dielectrics
2.3 The electromagnetic spectrum
2.4 Propagation of plane waves in materials
2.4.1 Propagation of plane waves in lossy dielectrics
2.4.2 The speed of propagation of waves and dispersion
2.4.3 Group velocity
2.4.4 Dispersion
2.4.5 Material properties
2.4.5.1 Static polarization and the concept of relative permittivity
2.4.5.2 Debye model for polarization
2.4.5.3 Lorentz model for polarization
2.4.6 Homogeneity, linearity, and anisotropy of materials
2.5 Reflection, transmission, refraction, scattering, and diffraction of electromagnetic waves
2.5.1 Reflection and transmission of electromagnetic waves at a general interface
2.5.2 Refraction, diffraction, and scattering of electromagnetic waves
2.6 Summary
References
3 Antennas: properties, designs, and optimization
3.1 Introduction
3.2 Antenna radiation parameters
3.2.1 Radiated power
3.2.2 Antenna radiation patterns
3.2.2.1 Antenna field radiation patterns
3.2.2.2 Antenna power radiation pattern
3.2.2.3 Beamwidth
3.2.3 Radiation intensity
3.2.4 Antenna directivity
3.2.5 Antenna gain
3.2.6 Polarization
3.2.7 Radiation resistance
3.2.8 Input impedance
3.2.9 Bandwidth
3.2.10 Pulse fidelity
3.2.11 Group delay
3.2.12 Receiving antenna parameters
3.2.13 Effective aperture
3.2.14 Antenna footprint
3.3 Antenna interaction with the medium under test
3.4 Antenna types for ground penetrating radar
3.4.1 Dipole antennas
3.4.2 Bowtie antennas
3.4.3 Vivaldi antennas
3.4.4 Spiral antennas
3.4.5 Horn antennas
3.4.6 Antenna arrays
3.5 Antenna design for GPR systems
3.5.1 GPR system parameters
3.5.1.1 Waveforms
3.5.1.2 System bandwidth
3.5.1.3 Antenna position
3.5.1.4 Legislation and standards
3.5.2 GPR antenna optimization framework
3.5.2.2 GPR antenna optimization goals
3.5.2.1 The electromagnetic model
3.6 The optimization process
3.6.1 The multi-objective genetic algorithm
3.6.2 Examples of optimization
3.6.2.1 Rounded bowtie antenna
3.6.2.2 Archimedean spiral antenna
3.6.2.3 Equiangular spiral antenna
3.6.2.4 Vivaldi antenna
3.6.2.5 U-slot patch antenna
3.6.3 Optimization for specific applications
3.6.3.1 Planar bowtie antenna optimization
3.6.3.2 V-shaped bowtie antenna optimization
3.6.3.3 Some remarks on optimization
References
4 The ground penetrating radar system
4.1 Introduction
4.2 Classification of ground penetrating radars
4.3 Requirements from ground penetrating radar
4.4 System specification
4.5 System requirements
4.5.1 Signal generator
4.5.1.1 GPR waveform types
4.5.2 Bandwidth
4.5.3 Amplifier
4.5.4 Power
4.5.5 Antennas
4.5.6 Low-noise amplifier
4.6 Data acquisition modes
4.6.1 Common offset mode
4.6.2 Common source and common receiver modes
4.6.3 Common midpoint mode
4.7 Signal processing
4.7.1 System abstraction
4.7.2 Digital signal conversion
4.7.3 Data processing
4.7.4 Preprocessing
4.7.4.1 Data editing
4.7.4.2 Time zero correction
4.7.4.3 Background subtraction
4.7.4.4 Downsampling
4.7.5 Basic signal processing
4.7.5.1 DC shift removal
4.7.5.2 Dewow filtering
4.7.5.3 Signal amplification and attenuation
4.7.5.4 Deconvolution
4.7.5.5 Matched filters
4.7.5.6 Band-pass filtering
4.7.5.7 Phase velocity analysis
4.7.5.8 Migration
4.7.5.9 Fโk wave filtering
4.7.6 Advanced signal processing
4.8 Summary
References
5 Numerical modeling
5.1 Introduction
5.2 Overview on EM modeling for GPR applications
5.3 Fundamentals of numerical methods commonly used for GPR modeling
5.3.1 The general idea of numerical solutions
5.3.2 A brief review of PDE-based numerical methods
5.3.2.1 The finite-difference time-domain method
5.3.2.2 The transmission-line matrix method
5.3.2.3 The finite element method
5.3.3 A brief review of integral-formula-based numerical methods
5.3.3.1 The method of moments
5.3.4 The boundary element method
5.4 Advantages and drawbacks of common modeling methods in GPR work
5.5 FDTD modeling of the GPR environment
5.5.1 FDTD for dispersive media
5.5.1.1 Source excitation
5.5.1.2 Nonuniform orthogonal grids
5.6 2D modeling of GPR applications using the FDTD method
5.6.1 Single steel rebar in concrete with frequency-independent properties
5.6.2 Multiple rebars and voids in concrete
5.7 3D modeling
5.7.1 Radar waveform synthesis
5.7.2 Input impedance calculation of bow-tie antennas
5.7.3 Bow-tie analysis using the method of moments
5.8 Modeling of practical geometries
5.8.1 Target shape scattering characteristics
5.9 Modeling of rough surface in a granular medium
5.10 Geophysical probing with electromagnetic wavesโuse of the transmission line method
5.11 Modeling dispersion from heterogeneous dielectricsโuse of the FDTD method
5.11.1 Model definition
5.12 Heterogeneity in a half-space
5.12.1 Distribution of changes in permittivity in one, two, and three directions
5.12.1.1 Results for random material properties with a standard deviation of 0.05
5.12.1.2 Results for random material properties with a standard deviation of 0.15
5.12.1.3 Results for random material properties with a standard deviation of 0.25
5.13 Boundaries and boundary conditions
5.14 PML optimization
5.14.1 Reflection from the PML boundary
5.14.2 The optimization process
5.14.3 Optimization results
5.15 Summary
References
6 Pattern recognition
6.1 Introduction
6.2 Inverse problems
6.2.1 Reverse-time migration algorithm
6.2.2 Pattern recognition algorithms (PRAs)
6.3 Pattern recognition methods applied to GPR
6.3.1 Buried cylinders in nonhomogeneous dielectric media: model fitting and hybrid migration-model fitting approaches
6.3.2 Buried cylinder in nonhomogeneous dielectric medium: the artificial neural network approach
6.3.2.1 Buried cylinder in non-homogeneous dielectric medium in the presence of noise: multi-objective artificial neural network
6.3.3 Buried cylinders in concrete: feature selection
6.3.3.1 Simulated and experimental GPR data
6.3.3.2 Feature selection results
6.4 Summary
References
Index
Back Cover
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