1979
DOI: 10.1109/tap.1979.1142036
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Optimization of planar arrays

Abstract: Absfruct-A method is developed for maximization of the gain of a planar array at a presmied sidelobe level. The method is iterative and includes a quadratic programming routine. Numerical examples are given for an octagonal array with a quadratic array lattice and for hexagonal arrays with triangular lattices. The latter arrays are compared to the results obtained by sampling the circular Taylor distriiution. W

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Cited by 19 publications
(9 citation statements)
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“…The control parameters of the DE algorithm have been set equal to the ones used in the previous simulations. The number of subarrays has been changed in the range [1,10]. Again, the population size has been set equal to ten times the number of unknowns.…”
Section: Numerical Simulationsmentioning
confidence: 99%
See 1 more Smart Citation
“…The control parameters of the DE algorithm have been set equal to the ones used in the previous simulations. The number of subarrays has been changed in the range [1,10]. Again, the population size has been set equal to ten times the number of unknowns.…”
Section: Numerical Simulationsmentioning
confidence: 99%
“…However, as specified in [5], the considered problem is only one of the possible synthesis problems. In many cases, the antenna design requires the optimization of the directivity of one of the patterns [1][9] [10]. Accordingly, in this letter, the approach in [5] is extended to the optimization of the directivity of the difference pattern.…”
mentioning
confidence: 99%
“…, M XP ; making the aperture illumination efficiency remain higher than a desired value ε; and finally making the absolute values of some desired N DR excitations not fall below certain specified limit κ R . We cannot, unlike in (Einarsson, 1979) (where a similar approach is followed), assume a real-valued field in all space since we are on the one hand considering real elements with interelement coupling and on the other hand making no assumptions on the geometry of the array and the main beam direction. This fact prevents the problem from being of quadratic programming type, requiring a more involved algorithm for its resolution.…”
Section: Inclusion Of Constraintsmentioning
confidence: 99%
“…More classic approaches range from the maximization of the ratio of two Hermitian forms (Cheng, 1971;Voges and Butler, 1972) to the use of Lagrange multipliers (Kurth, 1974), Newton methods (Morini et al, 2006), quadratic programming (Einarsson, 1979;Ng et al, 1993), biquadratic programming (Hirasawa, 1988) or L ∞ optimal methods (Shpak, 1996). Depending on the nature of the quantity to be maximized/minimized and the constraints, the problem may become a constrained non-linear optimization one, thus requiring the use of more involved methods for its resolution (Steykal et al, 1986;Jiao et al, 1993;Lebret and Boyd, 1997).…”
Section: Introductionmentioning
confidence: 99%
“…Equation (2) results from the ' array illumination (excitation coefficients). instance, a 7-element hexagonal array HA1 may be synthesized…”
mentioning
confidence: 99%