Abstract
This paper introduces a novel approach for the optimization of large-N fractal random antenna arrays using genetic algorithms. Genetic algorithms often become overwhelmed by large numbers of input parameters, leading to an inefficient optimization processes. Fractal-random geometries lend themselves well to genetic algorithm optimization through the ability to describe their complex array structures with only a small number of input parameters. In addition, the recursive properties of fractal-random arrays allow for the rapid calculation of the array factor, which can be exploited to significantly speed up the convergence of the GA. This paper describes a method that increases the number of generators used to construct the array as the optimization progresses, maximizing the benefit of using fractal-random geometries to describe large-N arrays. Several optimized solutions are discussed, the largest being a 1650 element linear fractal-random array with a -23.57 dB side-lobe level and a beamwidth of 0.05°.
Original language | English (US) |
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Article number | 1435959 |
Pages (from-to) | 922-926 |
Number of pages | 5 |
Journal | IEEE National Radar Conference - Proceedings |
Volume | 2005-January |
Issue number | January |
DOIs | |
State | Published - Jan 1 2005 |
Event | 2005 IEEE International Radar Conference Record, RADAR 2005 - Arlington, United States Duration: May 9 2005 → May 12 2005 |
All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering