Radiation pattern optimization for concentric circular antenna array with central element feeding using craziness-based particle swarm optimization
✍ Scribed by Durbadal Mandal; Sakti Prasad Ghoshal; Anup Kumar Bhattacharjee
- Publisher
- John Wiley and Sons
- Year
- 2010
- Tongue
- English
- Weight
- 591 KB
- Volume
- 20
- Category
- Article
- ISSN
- 1096-4290
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✦ Synopsis
In this article, two optimization heuristic search techniques, craziness-based particle swarm optimization (CRPSO) and CRPSO with wavelet mutation (CRPSOWM), are applied to the process of optimal designing three-ring concentric circular antenna arrays (CCAAs) focused on maximum sidelobe level (SLL) reduction. Hence, three-ring CCAA without and with central element feeding are considered. SLL is a critical radiation pattern parameter in the task of reducing background noise and interference in the most recent wireless communication systems. To improve the radiation pattern with maximum SLL reduction, an optimum set of antenna element parameters as excitation weights and radii of the rings is to be developed. This is an optimization problem dealing with complex, highly nonlinear, discontinuous, and nondifferentiable array factors of CCAA design. So, one evolutionary optimization technique as CRPSO is adopted. To enhance the optimization performance of CRPSO further in exploring the solution space more effectively for a better solution, the wavelet theory is again applied in terms of mutation. This wavelet mutationbased CRPSO is named as CRPSOWM. It is shown that the proposed CRPSOWM technique outperforms the CRPSO significantly in terms of convergence speed, solution quality, and solution robustness. Computational results finally reveal that among the various CCAA designs, the design containing central element and four, six, and eight elements in three successive concentric rings proves to be the near global optimal design with near global minimum SLL (À43.4 dB) as determined by CRPSOWM. Real coded genetic algorithm (RGA) and its wavelet-based improved version (RGAWM) as well are also adopted to compare the results of above particle swarm optimization-based algorithms. V