Contributions to the Performance Optimization of the Monopole Four Square Array Antenna [Elektronische Ressource] / Pedram Yazdanbakhsh. Gutachter: Klaus Sobach ; Dirk Manteuffel

Pedram

Informations

• Publié par: universitat_duisburg-essen
• Publié le: 01 janvier 2011
• Nombre de lectures: 40
• Langue: English
• Poids de l'ouvrage: 2 Mo

Extrait

Contributions to the Performance Optimization of
the Monopole Four Square Array Antenna





Von der Fakultät für Ingenieurwissenschaften der Abteilung EIT der Universität
Duisburg-Essen
zur Erlangung des akademischen Grades eines



Doktors der Ingenieurwissenschaften (Dr.-Ing.)


genehmigte Dissertation




Von
Pedram Yazdanbakhsh

aus
Mashhad / Iran







1. Gutachter: Prof. Dr.-Ing. Klaus Solbach

2. Gutachter: Prof. Dr.-Ing. Dirk Manteuffel


Tag der mündlichen Prüfung: 14. Oktober 2011








































iii

Abstract

Multi-beam antennas can be used for the sectorization of 360° azimuthal coverage. One
of the suitable realizations, where four monopole antennas are placed at the corners of a
square, is known as the “Monopole Four-Square Array Antenna”. This thesis presents
the optimization problem for this array antenna.

First, it is considered that this array is mounted on an infinite ground plane. With view
to practical applications, optimization criteria are defined and a genetic algorithm is
used to find the optimized values for the array variables.

Next, the “Monopole Four-Square Array Antenna” is considered to be mounted on a
finite ground plane (chassis). It is seen that all performance parameters of this array are
changed and deteriorated due to the excitation of chassis modes, which couple to and
between the monopole antennas and which radiate and produce diffraction at the edges
of the ground plane. It is found, that the performance is strongly affected by the size of
each antenna, the position of each antenna on the chassis as well as the size and shape of
the chassis.

A new optimization problem considering both the parameters of array and chassis
dimensions is defined and the optimal values are found using the method of genetic
algorithm. To model the chassis effects, in this step, the Theory of Characteristic Modes
for the chassis is introduced and the effect of chassis modes on the radiation patterns
and S-parameters are discussed. In order to allow the efficient use of the calculation-
extensive chassis modes in our optimization, an Artificial Neural Network (ANN) is set-
up to represent the effects of the chassis modes and the ANN is trained using results
from an EM-field simulator. In a further step, the remaining mutual coupling between
the elements of the monopole array is tackled. Using another ANN, a Decoupling and
Matching Network (DMN) is designed for the “Monopole Four-Square Array Antenna”
which considerably improves the radiation pattern.

The final “full-degree” optimization problem considers all parameters of the monopole
array on a small chassis as well as the variables in the DMN. It is shown that by
changing the values of the weighting coefficients in the optimization problem, the
resulting antenna design can be matched to priorities set by practical applications.








iv

Acknowledgments

I would like to express my special thanks to my supervisor Prof. Dr.-Ing. Klaus Solbach
for his expert guidance. He has taught me to do my research in an organized and
meticulous way.

I am also grateful to my second advisor Prof. Dr.-Ing. Dirk Manteuffel for his help and
valuable suggestions for this thesis and doctoral defense.

The members of the HFT (Hochfrequenztechnik) group at Duisburg-Essen University
have contributed immensely to my personal and professional time in Germany. The
group has been a source of friendships as well as good advice and collaboration.

My parents and all my family stood behind me for which I am thankful for them
forever.

Finally, I would like to thank my wife, Bahareh Elahi, for being extremely supportive
and patient throughout my graduate work.



























v

Contents


Abstract …........…………………………………………………………………... iii

Acknowledgments ………………………………………………………………... iv

List of Tables …………………………………………………………………….. viii

List of Figures ………………………………………………………………......... x

1 Introduction and Overview 1

2 Monopole Antenna and its Applications 3


2.1 Introduction………………………………………………………………... 3

2.2 Monopole Antenna………………….………………………...…………… 3


2.2.1 Radiation Mechanism….…………………………………………….. 4

2.2.2 Radiation Efficiency………….......………………………………….. 5

2.2.3 Self impedance…...…………………………………………………... 8


2.3 Planar Array Antenna……………………………….……………………... 11


2.3.1 Mutual Impedance……………………………………………………. 12


2.4 Monopole Four Square Array Antenna (MFSAA)………………………… 14


2.4.1 Radiation Mechanism….…………………………………………….. 15

2.4.2 Directivity…………….……………………………………………… 17

2.4.3 Envelope Correlation of Beams……………………………………… 18

2.4.4 Front-to-Back (F/B) ratio…….………………………………………. 19

2.4.5 Beam Crossover (BC) Level…………………………………………. 20

2.4.6 Fitting to the ideal secant squared elevation pattern…………………. 21

2.4.7 Maximum Absolute Gain of the MFSAA……………………………. 22


3 Optimization Methods and Neural Networks 24


3.1 Optimization Overview...…….......………………………………………... 24

Contents vi


3.1.1 Gradient-Based Optimization….…………………………..……….... 25

3.1.2 Direct Search Method………………………………………………... 25

3.1.3 Genetic Algorithm (GA)…….....……..……………………………… 26


3.2 Artificial Neural Networks (ANN) for system modelling………...……….. 31


3.2.1 How Neural Network works……........………………………............. 31

3.2.2 Multilayer Perceptron (MLP)………………………………………… 32


4 Performance optimization of the MFSAA 35


4.1 Optimization Problem …………..……………………………………….... 35


4.1.1 Minimum Envelope Correlation of Beams .......................................... 35

4.1.2 Best fit to the ideal secant-squared elevation pattern ………............... 35

4.1.3 Suitable Beam Crossover Level ………………….……...................... 36

4.1.4 Maximum Front-to-Back (F/B) Ratio………………………………... 36

4.1.5 Maximum Directivity………………………………………………… 36

4.1.6 Maximum Radiation Efficiency……………………………………… 37


4.2 First order approximation model ………………………………………….. 37


4.3 Second order approximation model …………………….…………………. 39


4.4 Third order approximation model ………..…………….…………………. 42

5 MFSAA on a finite ground plane (Chassis) 46


5.1 Theory of Characteristic Modes (TCM)…………………………………… 46


5.1.1 Mathematical formulation of characteristic modes………………... 46

5.1.2 A Neural Network model to calculate the chassis mode
eigenvalues……………………………………………………………….. 50


5.2 Effects of the finite ground plane (chassis)………………………………... 53

5.2.1 Single monopole antenna on a chassis …..……………………....... 53

5.2.2 Two monopole antennas on the chassis…………………………..... 59
Contents vii


5.2.3 MFSAA on the chassis…………………………………….……...… 61


5.3 Performance optimization of the MFSAA on chassis …………………….. 64

5.3.1 Minimize the reflection and coupling scattering coefficients of the
antennas……………………………………………………………….......... 64

5.3.2 Optimum Tolerance Region………………………………………….. 67

5.5.3 Realization of the MFSAA ……………………………….…………. 69

5.3.4 Realization of the feed network……………………………………… 70

5.3.5 Design of a Decoupling and Matching Network (DMN)-idealized….. 75

5.3.6 Design of a DMN-realistic effects…………………………………… 77

5.3.7 Full degree optimization……………………………………………... 82

5.3.7.1 Neural Network model for antenna plus DMN………………. 82

5.3.7.2 Optimization Problem………………………………………... 84

5.3.7.3 Optimization Problem and symmetric radiation pattern……... 86

5.3.7.4 Optimum Tolerance Region………………………………….. 88

5.3.8 Realization of the optimized MFSAA decoupled and matched……… 90

5.3.9 Changing the weighting coefficients of the opti