2014
DOI: 10.1109/tap.2013.2254446
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The Generalized Direct Optimization Technique for Printed Reflectarrays

Abstract: Abstract-A generalized direct optimization technique (GDOT)for

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Cited by 48 publications
(30 citation statements)
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“…This can be achieved either with a single layer of square patches [32], or with concentric squared loops and patches [33]. The use of this technique has made it possible to achieve a cross-polar discrimination (XPD) of 27 dB in a 20% bandwidth.…”
Section: Introductionmentioning
confidence: 99%
“…This can be achieved either with a single layer of square patches [32], or with concentric squared loops and patches [33]. The use of this technique has made it possible to achieve a cross-polar discrimination (XPD) of 27 dB in a 20% bandwidth.…”
Section: Introductionmentioning
confidence: 99%
“…For the minimization of the distance in (22), at this stage we have the element m as the trimmed gain G (u, v) and the current reflectarray geometry which generates a gain pattern that belongs to the R set, G(u, v). As a distance definition, the Euclidean norm for square-integrable functions can be used, which can be easily implemented by the weighted Euclidean metric…”
Section: B Backward Projectionmentioning
confidence: 99%
“…As stated in the previous section, due to the similarity between (24) and (26), the LMA is a natural choice to minimize the distance in (22), and in fact will be the minimizing algorithm used here. However, other gradient-based algorithms are also suitable, such as steepest descent [38], Gauss-Newton [39] or self-scaled BFGS [40] by setting up an appropriate cost function [28].…”
Section: Minimization Algorithm For the Geometry Optimizationmentioning
confidence: 99%
“…is used to update the velocity and the position of the coefficients ( αx_ij, βx_ij, and γx_ij) as a function of: (1) their previous velocity v¯true(t1true), (2) their previous position k¯true(t1true), (3) the best experience (position) of personal trueP¯ktrue(t1true), and (4) the best experience of group trueG¯ktrue(t1true). The so called “personal” and “group” are widely used in PSO optimization algorithm . While one step is done and the coefficients move toward a new position with new velocity, (or equivalently, coefficients get new values) these coefficients are used to calculate the radiation fields of the antenna.…”
Section: Synthesis Proceduresmentioning
confidence: 99%