Low Sidelobe Cosecant-Squared Pattern Synthesis for Large Planar Array Using Genetic Algorithm
Progress In Electromagnetics Research M • 2020
معلومات البحث
المؤلفون
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الكلمات المفتاحية
Not Available
المجلة العلمية
Progress In Electromagnetics Research M
الناشر
Not Available
المجلد
93
العدد
Not Available
الصفحات
23–34
publication.type
International
رابط البحث
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المواد المرفقة
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الملخص
A cosecant-squared radiation pattern synthesis for a planar antenna array by using the
genetic algorithm (GA) is presented. GA makes array synthesis flexible to achieve two desired features,
namely, low peak side lobe level (PSLL) and small deviation (ripples) in the shaped beam region. In
order to obtain a desired csc
2
pattern with the PSLL constrained, GA optimizes both the excitation
amplitude and phase weights of the array elements. Dynamic range ratio (DRR) of the excitation
amplitudes is improved by eliminating the weakly excited array elements from the optimized array
without distorting the obtained pattern. To illustrate the effectiveness and advantages of GA, the beam
pattern with specified characteristics is obtained for the same array by using particle swarm optimization
(PSO). Results show that the performances of GA and PSO are comparable when dealing with small-to-
moderate planar antenna arrays. However, GA significantly outperforms PSO on large arrays. Moreover,
numerical results reveal that GA is superior to PSO in terms of cost function evaluation and statistical
tests.
genetic algorithm (GA) is presented. GA makes array synthesis flexible to achieve two desired features,
namely, low peak side lobe level (PSLL) and small deviation (ripples) in the shaped beam region. In
order to obtain a desired csc
2
pattern with the PSLL constrained, GA optimizes both the excitation
amplitude and phase weights of the array elements. Dynamic range ratio (DRR) of the excitation
amplitudes is improved by eliminating the weakly excited array elements from the optimized array
without distorting the obtained pattern. To illustrate the effectiveness and advantages of GA, the beam
pattern with specified characteristics is obtained for the same array by using particle swarm optimization
(PSO). Results show that the performances of GA and PSO are comparable when dealing with small-to-
moderate planar antenna arrays. However, GA significantly outperforms PSO on large arrays. Moreover,
numerical results reveal that GA is superior to PSO in terms of cost function evaluation and statistical
tests.
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