Synthesis and design of thinned planar concentric circular antenna array - a multi-objective approach

Authors:
Sk. Minhazul Islam;Saurav Ghosh;Subhrajit Roy;Shizheng Zhao;Ponnuthurai Nagaratnam Suganthan;Swagamtam Das
Affiliations:
Dept. of Electronics and Telecommunication Engg., Jadavpur University, Kolkata, India;Dept. of Electronics and Telecommunication Engg., Jadavpur University, Kolkata, India;Dept. of Electronics and Telecommunication Engg., Jadavpur University, Kolkata, India;Dept. of Electronics and Electrical Engg., Nanyang Technological Univrsity, Singapore;Dept. of Electronics and Electrical Engg., Nanyang Technological Univrsity, Singapore;Dept. of Electronics and Telecommunication Engg., Jadavpur University, Kolkata, India
Venue:
SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part II
Year:
2011

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Abstract

Thinned concentric antenna array design is one of the most important electromagnetic optimization problems of current interest. This antenna must generate a pencil beam pattern in the vertical plane along with minimized side lobe level (SLL) and desired HPBW, FNBW and number of switched off elements. In this article, for the first time to the best of our knowledge, a multi-objective optimization framework for this design is presented. Four objectives described above we are treated as four distinct objectives that are to be optimized simultaneously. The multi-objective approach provides greater flexibility by yielding a set of equivalent final solutions from which the user can choose one that attains a suitable trade-off margin as per requirements. In this article, we have used a multi-objective algorithm of current interest namely the NSGA-II algorithm. There are two types of design, one with uniform inter-element spacing fixed at 0.5λ and the other with optimum uniform inter-element spacing. Extensive simulation and results are given with respect to the obtained HPBW, SLL, FNBW and number of switched off elements and compared with two state-of-the-art single objective optimization methods namely DE and PSO.