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العنوان
Design of planar antenna arrays using genetic algorithm /
الناشر
Ashraf El Tayib Ahmed ,
المؤلف
Ahmed, Ashraf El Tayib
هيئة الاعداد
باحث / اشرف الطيب احمد
مشرف / سعيد السيد اسماعيل الخامى
elkhamy@ieee.org
مشرف / منى ابراهيم حامد مصطفى لطفى
monai110@yahoo.com
مناقش / ابراهيم احمد سليم
مناقش / نور الدين حسن اسماعيل
uhassau58@live.com
الموضوع
Genetic algorithm .
تاريخ النشر
2002
عدد الصفحات
120 p :
اللغة
الإنجليزية
الدرجة
ماجستير
التخصص
الهندسة الكهربائية والالكترونية
تاريخ الإجازة
1/5/2002
مكان الإجازة
جامعة الاسكندريه - كلية الهندسة - الهندسة الكهربائيه
الفهرس
Only 14 pages are availabe for public view

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Abstract

The thesis deals with an important problem in antenna engineering, namely ,the design of planar arrays with emphasis on multi-rings circular and elliptical arrays configurations using GA as a modern optimization techniques.
Relative sidclobc level and pattern shapes have long been of interest to antenna designers. This interest has been high-toned by the jamming which threatens most military radars. The requirement for low sidelobes for clutter rejection in the AW ACS radar resulted in technology that now supports sidelobe levels of more than 50 dB below the main-beam peak.
The price that must be paid to achieve these low sidelobes includes; (1) a reduction in gain and directivity, (2) an increase in beamwidth, (3) an increase in tolerance control, (4) an increase in cost, and (5) to operate in an environment free from obstructions that can readily increase the sidelobes. In spite of these drawbacks, the trend to low-sidelobe antennas has accelerated since low-sidelobes provide an excellent counter to electronic countermeasures (l-X’.\’f). Sidelobe reduction is also used as a masking technique; to decrease the probability that the enemy can intercept the emissions of our radars at far distances.
In this thesis, a relatively new optimization technique, called GA is used for linear, planar, multi rings such as circular and elliptical arrays to obtain the lowest possible maximum relative-sidelobe level. The algorithm gives very good results.
The beauty of the GA is that its ability to optimize a large number of discrete parameters. Although algorithms tend to be slow (as in nature), they are very powerful. They can optimize problems with many parameters, and don’t require any gradient calculations.