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العنوان
Reduction of grating lobes for phased array antenna using genetic algorithm /
المؤلف
Omer, Ahmed Abdelmotelep Abdelfatah.
الموضوع
Electrical engineering.
تاريخ النشر
2010.
عدد الصفحات
141 p. :
اللغة
الإنجليزية
الدرجة
ماجستير
التخصص
الهندسة الكهربائية والالكترونية
تاريخ الإجازة
1/1/2010
مكان الإجازة
جامعة بنها - كلية الهندسة ببنها - electric engineering technology
الفهرس
Only 14 pages are availabe for public view

from 141

from 141

Abstract

The scan volume of a phased array is significantly reduced due to the appearance of grating lobes. These grating lobes should be eliminated in order to increase the scan volume. There are some reasons for grating lobe appearance, one of them is the spacing between elements, as the spacing between elements increases beyond a half wavelength the grating lobes begin to appear in visible space. Another reason is the amplitude and phase weighting at subarray outputs alone, this also contribute in grating lobes appearance due to the periodicity and larger spacing between subarrays.There are several methods used to eliminate or reduce grating lobes through the application of genetic algorithms including linear array, linear array divided into subarrays and planar array.Some methods are suitable for linear array, such as optimizing the spacing between elements.In this case, the nonuniform spacing was created by perturbing the element position. For more reduction, amplitude weighting at elements should be also optimized.Other methods are suitable for linear array divided into uniform subarrays with amplitude weightings and time delay at subarray level only, such as optimizing the subarray sizes and amplitude weights at subarray level.For more reduction, the spacing between the
elements is optimized.Other methods are suitable for reduction of grating lobes in planar array, such as optimizing the spacing between elements in x and y-direction, including the amplitude weights at elements with the spacing to be optimized and optimizing the position of all elements along the array.These methods will be optimized using genetic algorithm implemented in MATLAB.