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E-raamat: Computational Methods for Electromagnetic Inverse Scattering [Wiley Online]

  • Formaat: 328 pages
  • Sari: IEEE Press
  • Ilmumisaeg: 12-Jun-2018
  • Kirjastus: Wiley-IEEE Press
  • ISBN-10: 1119311993
  • ISBN-13: 9781119311997
Teised raamatud teemal:
  • Wiley Online
  • Hind: 148,02 €*
  • * hind, mis tagab piiramatu üheaegsete kasutajate arvuga ligipääsu piiramatuks ajaks
Computational Methods for Electromagnetic Inverse ScatteringSuurem pilt
  • Formaat: 328 pages
  • Sari: IEEE Press
  • Ilmumisaeg: 12-Jun-2018
  • Kirjastus: Wiley-IEEE Press
  • ISBN-10: 1119311993
  • ISBN-13: 9781119311997
Teised raamatud teemal:
Wiley Online e-raamatudRohkem infot Wiley Online kohta
Raamatu kodulehekülg: https://onlinelibrary.wiley.com/doi/book/10.1002/9781119311997

A comprehensive and updated overview of the theory, algorithms and applications of for electromagnetic inverse scattering problems

  • Offers the recent and most important advances in inverse scattering grounded in fundamental theory, algorithms and practical engineering applications
  • Covers the latest, most relevant inverse scattering techniques like signal subspace methods, time reversal, linear sampling, qualitative methods, compressive sensing, and noniterative methods
  • Emphasizes theory, mathematical derivation and physical insights of various inverse scattering problems
  • Written by a leading expert in the field
Foreword xiii
Preface xv
1 Introduction 1(12)
1.1 Introduction to Electromagnetic Inverse Scattering Problems
1(1)
1.2 Forward Scattering Problems
2(1)
1.3 Properties of Inverse Scattering Problems
3(3)
1.4 Scope of the Book
6(3)
References
9(4)
2 Fundamentals of Electromagnetic Wave Theory 13(28)
2.1 Maxwell's Equations
13(3)
2.1.1 Representations in Differential Form
13(1)
2.1.2 Time-Harmonic Forms
14(1)
2.1.3 Boundary Conditions
15(1)
2.1.4 Constitutive Relations
16(1)
2.2 General Description of a Scattering Problem
16(2)
2.3 Duality Principle
18(1)
2.4 Radiation in Free Space
18(2)
2.5 Volume Integral Equations for Dielectric Scatterers
20(1)
2.6 Surface Integral Equations for Perfectly Conducting Scatterers
21(1)
2.7 Two-Dimensional Scattering Problems
22(2)
2.8 Scattering by Small Scatterers
24(5)
2.8.1 Three-Dimensional Case
24(3)
2.8.2 Two-Dimensional Case
27(1)
2.8.3 Scattering by a Collection of Small Scatterers
28(1)
2.8.4 Degrees of Freedom
28(1)
2.9 Scattering by Extended Scatterers
29(3)
2.9.1 Nonmagnetic Dielectric Scatterers
29(2)
2.9.2 Perfectly Electrically Conducting Scatterers
31(1)
2.10 Far-Field Approximation
32(2)
2.11 Reciprocity
34(1)
2.12 Huygens' Principle and Extinction Theorem
35(4)
References
39(2)
3 Time-Reversal Imaging 41(26)
3.1 Time-Reversal Imaging for Active Sources
41(12)
3.1.1 Explanation Based on Geometrical Optics
41(2)
3.1.2 Implementation Steps
43(2)
3.1.3 Fundamental Theory
45(3)
3.1.4 Analysis of Resolution
48(1)
3.1.5 Vectorial Wave
49(4)
3.2 Time-Reversal Imaging for Passive Sources
53(9)
3.2.1 Imaging by an Iterative Time-Reversal Process
54(1)
3.2.2 Imaging by the DORT Method
55(1)
3.2.3 Numerical Simulations
56(6)
3.3 Discussions
62(2)
References
64(3)
4 Inverse Scattering Problems of Small Scatterers 67(36)
4.1 Forward Problem: Foldy-Lax Equation
68(1)
4.2 Uniqueness Theorem for the Inverse Problem
69(4)
4.2.1 Inverse Source Problem
70(1)
4.2.2 Inverse Scattering Problem
71(2)
Locating Positions
72(1)
Retrieving Scattering Strength
72(1)
4.3 Numerical Methods
73(6)
4.3.1 Multiple Signal Classification Imaging
73(4)
4.3.2 Noniterative Retrieval of Scattering Strength
77(2)
4.4 Inversion of a Vector Wave Equation
79(18)
4.4.1 Forward Problem
79(3)
4.4.2 Multiple Signal Classification Imaging
82(6)
Nondegenerate Case
82(1)
Degenerate Case
83(5)
4.4.3 Noniterative Retrieval of Scattering Strength Tensors
88(2)
4.4.4 Subspace Imaging Algorithm with Enhanced Resolution
90(7)
4.5 Discussions
97(2)
References
99(4)
5 Linear Sampling Method 103(20)
5.1 Outline of the Linear Sampling Method
104(2)
5.2 Physical Interpretation
106(3)
5.2.1 Source Distribution
106(2)
5.2.2 Multipole Radiation
108(1)
5.3 Multipole-Based Linear Sampling Method
109(7)
5.3.1 Description of the Algorithm
109(1)
5.3.2 Choice of the Number of Multipoles
110(3)
5.3.3 Comparison with Tikhonov Regularization
113(1)
5.3.4 Numerical Examples
114(2)
5.4 Factorization Method
116(2)
5.5 Discussions
118(1)
References
119(4)
6 Reconstructing Dielectric Scatterers 123(60)
6.1 Introduction
124(5)
6.1.1 Uniqueness, Stability, and Nonlinearity
124(2)
6.1.2 Formulation of the Forward Problem
126(1)
6.1.3 Optimization Approach to the Inverse Problem
127(2)
6.2 Noniterative Inversion Methods
129(10)
6.2.1 Born Approximation Inversion Method
130(1)
6.2.2 Rytov Approximation Inversion Method
130(1)
6.2.3 Extended Born Approximation Inversion Method
131(2)
6.2.4 Back-Propagation Scheme
133(1)
6.2.5 Numerical Examples
134(5)
6.3 Full-Wave Iterative Inversion Methods
139(10)
6.3.1 Distorted Born Iterative Method
139(3)
6.3.2 Contrast Source Inversion Method
142(2)
6.3.3 Contrast Source Extended Born Method
144(2)
6.3.4 Other Iterative Models
146(3)
6.4 Subspace-Based Optimization Method (SOM)
149(22)
6.4.1 Gs-SOM
149(12)
6.4.2 Twofold SOM
161(3)
6.4.3 New Fast Fourier Transform SOM
164(5)
6.4.4 SOM for the Vector Wave
169(2)
6.5 Discussions
171(3)
References
174(9)
7 Reconstructing Perfect Electric Conductors 183(24)
7.1 Introduction
183(2)
7.1.1 Formulation of the Forward Problem
183(1)
7.1.2 Uniqueness and Stability
184(1)
7.2 Inversion Models Requiring Prior Information
185(1)
7.3 Inversion Models Without Prior Information
186(10)
7.3.1 Transverse-Magnetic Case
187(5)
7.3.2 Transverse-Electric Case
192(4)
7.4 Mixture of PEC and Dielectric Scatterers
196(6)
7.5 Discussions
202(1)
References
203(4)
8 inversion for Phaseless Data 207(20)
8.1 Introduction
207(2)
8.2 Reconstructing Point-Like Scatterers by Subspace Methods
209(5)
8.2.1 Converting a Nonlinear Problem to a Linear One
210(2)
8.2.2 Rank of the Multistatic Response Matrix
212(1)
8.2.3 MUSIC Localization and Noniterative Retrieval
213(1)
8.3 Reconstructing Point-Like Scatterers by Compressive Sensing
214(6)
8.3.1 Introduction to Compressive Sensing
214(1)
8.3.2 Solving Phase-Available Inverse Problems by CS
215(1)
8.3.3 Solving Phaseless Inverse Problems by CS
216(2)
8.3.4 Applicability of CS
218(1)
8.3.5 Numerical Examples
219(1)
8.4 Reconstructing Extended Dielectric Scatterers
220(3)
8.5 Discussions
223(1)
References
224(3)
9 Inversion with an Inhomogeneous Background Medium 227(30)
9.1 Introduction
227(2)
9.2 Integral Equation Approach via Numerical Green's Function
229(6)
9.3 Differential Equation Approach
235(5)
9.4 Homogeneous Background Approach
240(3)
9.5 Examples of Three-Dimensional Problems
243(9)
9.5.1 Confocal Laser Scanning Microscope
246(3)
9.5.2 Near-Field Scanning Microwave Impedance Microscopy
249(3)
9.6 Discussions
252(2)
References
254(3)
10 Resolution of Computational Imaging 257(24)
10.1 Diffraction-Limited Imaging System
257(4)
10.2 Computational Imaging
261(3)
10.2.1 Inverse Source Problem
261(1)
10.2.2 Inverse Scattering Problem
262(2)
10.3 Cramer-Rao Bound
264(4)
10.4 Resolution under the Born Approximation
268(4)
10.5 Discussions
272(5)
10.6 Summary
277(1)
References
278(3)
Appendices
A Ill-Posed Problems and Regularization
281(10)
A.1 Ill-Posed Problems
281(1)
A.2 Regularization Theory
282(1)
A.3 Regularization Schemes
283(3)
A.3.1 Spectral Cutoff
284(1)
A.3.2 Tikhonov Regularization
285(1)
A.3.3 Iterative Regularization
285(1)
A.4 Regularization Parameter Selection Methods
286(2)
A.4.1 Discrepancy Principle
287(1)
A.4.2 Generalized Cross Validation
287(1)
A.4.3 L-Curve Method
287(1)
A.4.4 Trial and Error
288(1)
A.5 Discussions
288(3)
B Least Squares
291(4)
B.1 Geometric Interpretation of Least Squares
291(1)
B.1.1 Real Space
291(1)
B.1.2 Complex Space
292(1)
B.2 Gradient of Squared Residuals
292(3)
C Conjugate Gradient Method
295(4)
C.1 Solving General Minimization Problems
295(1)
C.1.1 Real Space
295(1)
C.1.2 Complex Space
296(1)
C.2 Solving Linear Equation Systems
296(3)
D Matrix-Vector Product by the FFT Procedure
299(2)
D.1 One-Dimensional Case
299(1)
D.2 Two-Dimensional Case
300(1)
Appendix References
301(2)
Index 303
Xudong Chen, received the B.S. and M.S. degrees in electrical engineering from Zhejiang University, Hangzhou, China, in 1999 and 2001, respectively, and the Ph.D. degree from the Massachusetts Institute of Technology, Cambridge, MA, USA, in 2005. Since then he joined the Department of Electrical and Computer Engineering, National University of Singapore, Singapore, and he is currently an Associate Professor. His research interests include mainly electromagnetic inverse problems. He has published more than 120 peer-reviewed journal papers on inverse scattering problems, material parameter retrieval, and optical encryption. The total citation of his papers is about 2,500 according to ISI Web of Science till Dec 2015. He visited the University of Paris-SUD 11 in May-June 2010 as an invited visiting Associate Professor. He was the recipient of the Young Scientist Award by the Union Radio-Scientifique Internationale (URSI) in 2010 and Engineering Young Researcher Award by FOE, National University of Singapore in 2015. He is currently an Associate Editor of the IEEE Transactions on Microwave Theory and Techniques.
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