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

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...

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Bibliographic Details
Published in:IEEE transactions on antennas and propagation 2005-08, Vol.53 (8), p.2674-2679
Main Authors: Khodier, M.M., Christodoulou, C.G.
Format: Article
Language:English
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
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Summary: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).
ISSN:0018-926X
1558-2221
DOI:10.1109/TAP.2005.851762