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E-raamat: Simulation-based Optimization Of Antenna Arrays

(Reykjavik Univ, Iceland), (Reykjavik Univ, Iceland)
  • Formaat: 496 pages
  • Ilmumisaeg: 13-Feb-2019
  • Kirjastus: World Scientific Europe Ltd
  • Keel: eng
  • ISBN-13: 9781786346001
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Simulation-based Optimization Of Antenna ArraysSuurem pilt
  • Formaat: 496 pages
  • Ilmumisaeg: 13-Feb-2019
  • Kirjastus: World Scientific Europe Ltd
  • Keel: eng
  • ISBN-13: 9781786346001
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The book addresses surrogate-assisted design of antenna arrays, in particular, how surrogate models, both data-driven and physics-based, can be utilized to expedite procedures such as parametric optimization, design closure, statistical analysis, or fault detection. Algorithms and design frameworks are illustrated using a large variety of examples including real-world printed-circuit antenna and antenna array structures. This unique compendium contains introductory materials concerning numerical optimization, both conventional (gradient-based and derivative-free, including metaheuristics) and surrogate-based, as well as a considerable selection of customized procedures developed specifically to handle antenna array problems. Recommendations concerning practical aspects of surrogate-assisted multi-objective antenna optimization are also given. The methods presented allow for cost-efficient handling of antenna array design problems (involving CPU-intensive EM models) in the context of design optimization and statistical analysis, which will benefit both researchers, designers and graduate students.

Preface vii
About the Authors ix
Acknowledgments xi
1 Introduction
1(10)
2 Antenna Array Fundamentals
11(22)
2.1 Introduction
11(3)
2.2 Antenna Array Radiation Figures
14(3)
2.3 Array Factor Analysis
17(5)
2.4 Mutual Coupling and Antenna Array Reflection Response
22(4)
2.5 Mutual Coupling and Antenna Array Design Validation
26(3)
2.6 Simulation-Based Antenna Array Design
29(1)
2.7 A Concept of Antenna Array Simulation-Based Optimization
30(1)
2.8 Challenges of Antenna Array Simulation-Based Optimization
31(2)
3 Fundamentals of Numerical Optimization
33(26)
3.1 Formulation of the Optimization Problem
34(1)
3.2 Gradient-Based Optimization
35(16)
3.2.1 Optimization using descent methods
37(5)
3.2.2 Newton and quasi-Newton methods
42(4)
3.2.3 Qualitative comparison of descent methods
46(1)
3.2.4 Remarks on constrained optimization
46(5)
3.3 Derivative-Free Optimization
51(6)
3.3.1 Pattern search methods
53(1)
3.3.2 Hooke-Jeeves direct search
54(1)
3.3.3 Nelder-Mead algorithm
55(2)
3.4 Summary
57(2)
4 Global Optimization: Population-Based Metaheuristics
59(26)
4.1 Fundamentals of Population-Based Metaheuristics
61(4)
4.2 Evolution Strategies
65(3)
4.3 Genetic Algorithms
68(6)
4.3.1 Algorithm flow and representation
68(1)
4.3.2 Crossover
69(1)
4.3.3 Mutation
70(1)
4.3.4 Selection
71(1)
4.3.5 Elitism
72(1)
4.3.6 Selected topics
72(2)
4.4 Evolutionary Algorithms
74(2)
4.5 Particle Swarm Optimization
76(1)
4.6 Differential Evolution
77(2)
4.7 Firefly Algorithm
79(2)
4.8 Other Methods
81(1)
4.9 Summary
81(4)
5 Fundamentals of Surrogate-Based Modeling and Optimization
85(48)
5.1 Surrogate-Based Optimization
86(5)
5.2 Surrogate Modeling: Data-Driven Surrogates
91(11)
5.2.1 Modeling flow for data-driven surrogates
92(1)
5.2.2 Design of experiments
92(2)
5.2.3 Data-driven modeling techniques
94(1)
5.2.3.1 Polynomial regression
95(1)
5.2.3.2 Radial basis functions
96(1)
5.2.3.3 Kriging
96(2)
5.2.3.4 Artificial neural networks
98(1)
5.2.3.5 Support vector regression
99(1)
5.2.3.6 Other approximation methods
100(1)
5.2.4 Model validation
101(1)
5.3 Surrogate Modeling: Physics-Based Surrogates
102(7)
5.4 Optimization with Data-Driven Surrogates
109(6)
5.4.1 Optimization by means of response surfaces
109(1)
5.4.2 Sequential approximate optimization
110(3)
5.4.3 SBO with kriging surrogates: Exploration versus exploitation
113(2)
5.4.4 Summary
115(1)
5.5 SBO Using Physics-Based Surrogates
115(18)
5.5.1 Space mapping
116(3)
5.5.2 Approximation model management optimization
119(1)
5.5.3 Manifold mapping
119(1)
5.5.4 Shape preserving response prediction
120(1)
5.5.5 Adaptively adjusted design specifications
121(3)
5.5.6 Feature-based optimization
124(6)
5.5.7 Summary
130(3)
6 Antenna Models for Simulation-Based Design
133(12)
6.1 Low-Fidelity Antenna Models in Simulation-Based Optimization
133(3)
6.2 Coarse-Discretization as a Basis of Low-Fidelity Models
136(3)
6.3 Other Simplifications of Low-Fidelity Models
139(4)
6.4 Automated Selection of Model Fidelity
143(2)
7 Element Design: Case Studies
145(38)
7.1 EM-Driven Design of a Planar UWB Dipole Antenna with Integrated Balun
145(3)
7.1.1 Antenna geometry
146(1)
7.1.2 Optimization procedure
146(2)
7.1.3 Numerical results
148(1)
7.1.4 Experimental validation
148(1)
7.2 Design of Compact UWB Slot Antenna
148(7)
7.2.1 Antenna structure
149(2)
7.2.2 Optimization algorithm
151(1)
7.2.3 Numerical results and experimental validation
152(3)
7.3 Optimization of Slot-Ring Coupled Patch Antenna
155(5)
7.3.1 Antenna structure
156(1)
7.3.2 Low-fidelity model selection
156(3)
7.3.3 Results, benchmarking, and experimental validation
159(1)
7.4 Low-Cost Modeling and Optimization of Ring Slot Antenna
160(8)
7.4.1 Modeling methodology
161(3)
7.4.2 Ring slot antenna structure
164(2)
7.4.3 Application examples and experimental validation
166(2)
7.5 Multi-objective Design of Planar Yagi Antenna
168(6)
7.5.1 Antenna structure and problem statement
169(1)
7.5.2 Design procedure and numerical results
170(3)
7.5.3 Experimental validation
173(1)
7.6 Design of Microstrip Patch Antennas
174(9)
7.6.1 Recessed microstrip line fed MPA: Geometry and model setup
176(1)
7.6.2 Recessed microstrip line fed MPA: Optimization procedure
177(1)
7.6.3 Recessed microstrip line fed MPA: Numerical results and experimental validation
177(1)
7.6.4 Slot-energized MPA: Geometry and model setup
177(2)
7.6.5 Slot-energized MPA: Design with optimization, results, and validation
179(4)
8 Microstrip Antenna Subarray Design Using Simulation-Based Optimization
183(20)
8.1 Design Method: Optimization Algorithm
184(4)
8.2 Design Formulation and EM Models of Microstrip Antenna Subarrays
188(5)
8.2.1 MPAS I (one-side configuration)
190(1)
8.2.2 MPAS II (two-side configuration)
190(3)
8.3 Optimization Results
193(6)
8.3.1 MPAS I (one-side configuration)
194(3)
8.3.2 MPAS II (two-side configuration)
197(2)
8.4 Validation by Measurements
199(2)
8.5 Summary
201(2)
9 Antenna Array Models for Simulation-Based Design and Optimization
203(10)
9.1 Array Factor-Based Models of Antenna Array Apertures
205(3)
9.2 Computational EM Models of Antenna Arrays
208(4)
9.3 Simulation-Based Superposition Models
212(1)
10 Design of Linear Antenna Array Apertures Using Surrogate-Assisted Optimization
213(40)
10.1 Optimum Design of Array Factor Models Using Smart Random Search and Gradient-Based Optimization
214(9)
10.1.1 Problem formulation
214(1)
10.1.2 Optimization methodology
215(1)
10.1.3 Case study 1: Linear end-fire array optimization
216(1)
10.1.3.1 Sidelobe reduction with two variables
216(2)
10.1.3.2 Peak directivity maximization with two variables
218(1)
10.1.3.3 Sidelobe reduction with different phase shifts
219(3)
10.1.3.4 Sidelobe reduction with different phase shifts and spacing
222(1)
10.2 Null Controlled Pattern Design
223(6)
10.3 20-Element Broadside Array Design for Pattern Nulls and Sector Beam
229(2)
10.4 Phase-Spacing Optimization of Linear Arrays Using Simulation-Based Surrogate Superposition Models
231(20)
10.4.1 Array aperture geometry and design problem outline
231(2)
10.4.2 Array factor model for the radiation response estimation
233(1)
10.4.3 Design using optimization of simulation-based surrogates
234(1)
10.4.3.1 Problem formulation: Objective function
234(1)
10.4.3.2 Optimization of the array factor model
235(1)
10.4.3.3 Correction of the simulation-based low-fidelity model
235(2)
10.4.3.4 Optimization algorithm
237(1)
10.4.4 Optimization results
238(1)
10.4.4.1 Optimization with non-uniform spacing and phases
239(5)
10.4.4.2 Optimization with uniform spacing and non-uniform phases
244(2)
10.4.5 Optimized designs as phased array apertures
246(5)
10.5 Summary
251(2)
11 Design of Planar Microstrip Antenna Arrays Using Variable-Fidelity EM Models
253(20)
11.1 Planar Antenna Array Design Problem
254(1)
11.2 Design Optimization Methodology
255(5)
11.2.1 Surrogate-based optimization
255(2)
11.2.2 Surrogate-based optimization for array design
257(3)
11.3 Implementation and Numerical Results
260(4)
11.4 Rapid Optimization of Radiation Response
264(6)
11.4.1 Design case: 49-element microstrip array
264(2)
11.4.2 Utilized models
266(2)
11.4.3 Optimization with non-uniform amplitude excitation
268(1)
11.4.4 Optimization with non-uniform phase excitation
269(1)
11.5 Summary
270(3)
12 Design of Planar Microstrip Array Antennas Using Simulation-Based Superposition Models
273(18)
12.1 Design Problem and Array Models
274(2)
12.1.1 Design problem and antenna array geometry
274(1)
12.1.2 Superposition models and discrete EM models
274(2)
12.2 Design Optimization Using Surrogates
276(3)
12.2.1 Design problem formulation: Objective function
276(1)
12.2.2 Low-fidelity model: Model correction
277(1)
12.2.3 Optimization algorithm
278(1)
12.3 Results
279(8)
12.3.1 16-Element Cartesian lattice antenna array
280(2)
12.3.2 100-Element Cartesian lattice antenna array
282(4)
12.3.3 100-Element hexagonal antenna array
286(1)
12.4 Summary
287(4)
13 Design of Planar Arrays Using Radiation Response Surrogates
291(10)
13.1 Design Optimization Methodology
291(4)
13.1.1 Problem formulation
292(1)
13.1.2 Response correction of array factor model
292(2)
13.1.3 Optimization flow
294(1)
13.2 Case Study I: 100-Element Microstrip Patch Antenna Array
295(3)
13.3 Case Study II: 28-Element Microstrip Patch Antenna Array
298(2)
13.4 Summary
300(1)
14 Simulation-Based Design of Corporate Feeds for Low-Sidelobe Microstrip Linear Arrays
301(56)
14.1 Sidelobe Reduction in Arrays Driven with Corporate Feeds Comprising Equal Power Split Junctions
303(25)
14.1.1 Approach justification
303(2)
14.1.2 Design task, feed elements, and feed architectures
305(2)
14.1.3 Fast models of corporate feeds
307(6)
14.1.4 Realization and simulation-based optimization of aperture-feed structures
313(9)
14.1.5 Validation
322(6)
14.2 Design of Low-Sidelobe Arrays Implementing Requited Excitation Tapers: The Case of Corporate Feeds Comprising Unequal-Split Junctions
328(27)
14.2.1 Approach justification
329(1)
14.2.2 Design process
330(4)
14.2.2.1 Modeling and optimization of unequal-split junctions
334(2)
14.2.2.2 Feed redesign for sidelobe minimization
336(2)
14.2.3 Realization of the design process: Numerical results and measurements
338(1)
14.2.3.1 Example of a taper-oriented design
339(4)
14.2.3.2 Example of an SLL-oriented design
343(12)
14.3 Summary
355(2)
15 Design of Linear Phased Array Apertures Using Response Correction and Surrogate-Assisted Optimization
357(16)
15.1 Optimization Methodology
358(4)
15.1.1 Element optimization
358(1)
15.1.2 Correction of the array factor model
359(1)
15.1.3 Design for scanning
360(2)
15.2 Case Study: 16-Element Linear Phased Array
362(5)
15.2.1 Element optimization
362(2)
15.2.2 Optimal excitation taper for the major lobe pointing broadside
364(1)
15.2.3 Optimization for major lobe scanning
365(1)
15.2.4 Experimental validation of the optimal design
365(2)
15.3 Optimal Design as the Phased Array
367(3)
15.4 Summary
370(3)
16 Fault Detection in Linear Arrays Using Response Correction Techniques
373(20)
16.1 Fault Detection in Array Antennas
374(2)
16.2 Fault Detection Methodology
376(9)
16.2.1 Surrogate model construction
376(5)
16.2.2 Fault detection using fast enumeration
381(4)
16.3 Numerical Results
385(5)
16.3.1 On-off faults
385(4)
16.3.2 Partial faults
389(1)
16.4 Summary
390(3)
17 Surrogate-Assisted Tolerance Analysis of Microstrip Linear Arrays with Corporate Feeds
393(16)
17.1 Manufacturing Tolerances in Linear Antenna Arrays with Corporate Feeds
394(1)
17.2 Local Surrogate Modeling of Antenna Array Apertures
395(4)
17.2.1 Radiation pattern surrogate of array elements
395(1)
17.2.2 Local modeling of array aperture: Array factor model
396(1)
17.2.3 Local modeling of array aperture: Model correction using response features
396(3)
17.3 Local Surrogate Modeling of Corporate Feeds
399(1)
17.4 Case Study: 12-Element Microstrip Array
400(9)
17.4.1 Array structure
400(3)
17.4.2 Results and discussion
403(6)
18 Discussion and Recommendations: Prospective Look
409(8)
References 417(24)
Index 441
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