Linear array geometry synthesis with minimum sidelobe level and null control using particle swarm optimization

• Published in: IEEE transactions on antennas and propagation Vol. 53; no. 8; pp. 2674 - 2679
• Main Authors: Khodier, M.M., Christodoulou, C.G.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.08.2005; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithm design and analysis; Algorithms; Antenna array; Antenna arrays; Antennas; Applied sciences; Design optimization; Evolutionary algorithms; Evolutionary computation; Exact sciences and technology; Genetic algorithms; Geometry; Linear antenna arrays; Linear arrays; Mathematical analysis; null control; Optimization; Particle swarm optimization; particle swarm optimization (PSO); Phased arrays; Radiocommunications; sidelobe suppression; Sidelobes; Simulated annealing; Studies; Synthesis; Telecommunications; Telecommunications and information theory; Lateral lobe; Linear antenna; null control; System design; sidelobe suppression; particle swarm optimization (PSO); Antenna array; Particle swarm optimization; Antenna synthesis
• ISSN: 0018-926X; 1558-2221
• DOI: 10.1109/TAP.2005.851762

This paper describes the synthesis method of linear array geometry with minimum sidelobe level and null control using the particle swarm optimization (PSO) algorithm. The PSO algorithm is a newly discovered, high-performance evolutionary algorithm capable of solving general N-dimensional, linear and nonlinear optimization problems. Compared to other evolutionary methods such as genetic algorithms and simulated annealing, the PSO algorithm is much easier to understand and implement and requires the least of mathematical preprocessing. The array geometry synthesis is first formulated as an optimization problem with the goal of sidelobe level (SLL) suppression and/or null placement in certain directions, and then solved by the PSO algorithm for the optimum element locations. Three design examples are presented that illustrate the use of the PSO algorithm, and the optimization goal in each example is easily achieved. The results of the PSO algorithm are validated by comparing with results obtained using the quadratic programming method (QPM).