PEEC-Based Multi-Objective Synthesis of Non-Uniformly Spaced Linear Antenna Arrays

• Published in: IEEE transactions on magnetics Vol. 53; no. 6; pp. 1 - 4
• Main Authors: Bauernfeind, Thomas, Baumgartner, Paul, Biro, Oszkar, Magele, Christian A., Preis, Kurt, Torchio, Riccardo
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Antenna arrays; Antennas; Clustering; Coupling; Dipole antennas; Electric fields; Equivalent circuits; Finite element analysis; Linear antenna arrays; Linear programming; Lobes; Magnetism; Multiple objective analysis; Multiplication; Optimization; partial element equivalent circuit (PEEC); Particle swarm optimization; particle swarm optimization (PSO); Probability theory; Wireless communication systems; Wireless communications
• ISSN: 0018-9464; 1941-0069
• DOI: 10.1109/TMAG.2017.2670679

Linear antenna arrays consisting of dipole antennas find wide applications in wireless communication systems. Typically, arrangements with uniform spacing between the array elements are applied to gain highly directive radiation patterns while controlling the radiation intensity in the side lobes. To describe such arrangements, the so-called pattern multiplication (PM) technique is frequently applied, which inherently neglects the inter-element coupling. In this paper, an antenna array arrangement with non-uniformly spacing between the array elements is synthesized in the multi-objective sense. Therefore, the PM technique is deficient. Hence, numerical techniques have to be applied to compute the electromagnetic field during the optimization process. Due to its simplicity and the consequently reduced computational costs, the partial element equivalent circuit method is applied within the field computation process. The optimization relies on a stochastic particle swarm optimization strategy, namely the firefly algorithm. In this paper, an extended version of the general algorithm with additional hierarchical clustering is applied. Special attention is drawn to the development of a suitable objective function.