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Articles

A novel strategy for the synthesis of thinned planar antenna array which furnishes lowest possible peak side lobe level without appearance of grating lobes over wide steering angles

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Pages 842-857 | Received 09 Jul 2017, Accepted 30 Sep 2017, Published online: 29 Nov 2017
 

Abstract

In this paper, we propose a novel strategy for the synthesis of uniformly excited thinned planar antenna (UE-TPA) arrays which renders maximum possible reduction in the peak side lobe level (PSLL) with minimum number of turn ‘ON’ elements over wide steering angles. The main objective of this paper is to investigate an efficient variant of genetic algorithm (GA) which yields good quality solution at affordable computational complexity. Towards this aim, authors attempted to develop a powerful synthesis tool denoted as improved binary coded GA (IBc-GA) by investigating novel chromosome arrangements and ameliorated process techniques for crossover and mutation operations to improve the flexibility as well as the global search ability in achieving the best possible reduction in PSLL without increasing computational burden. In order to corroborate the effectiveness and superiority of the proposed synthesis technique, two examples consisting of 10 × 10- and 14 × 14-elements UE-TPA arrays are numerically examined at antenna boresight as well as when the arrays are electronically steered up to 40° with respect to antenna boresight in both azimuth and elevation planes. The numerical results of 10 × 10-element UE-TPA obtained through the proposed synthesis method at antenna boresight are compared with those determined at 20° and 40° scan angles and with those of same size arrays reported in the literature. In addition, results at antenna boresight achieved by proposed method for the case of 14 × 14-element UE-TPA array are compared with those determined at 20° and 40° scan angles. It is inferred from the investigation that wide angle scanning with lowest possible PSLL and without appearance of grating lobes is achieved at comparable computational cost.

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