Thinned arrays using genetic algorithms

Haupt, Randy L.; Menozzi, J.J.; McCormack, C.J.

doi:10.1109/aps.1993.385248

Cited by 162 publications

(262 citation statements)

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“…Radiation pattern and element configuration of 50 elements are shown in figs. [5][6].The maximum relative side lobe level obtained is -36.59 . The sidelobe level is -33.47 for 64% filling.…”

Section: Element Pattern Of Half Wave Dipolementioning

confidence: 89%

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Optimization of Thinned Dipole Arrays Using Genetic Algorithm

Lakshmi¹

2011

IJET

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Abstract-Array synthesis is usually done by controlling amplitude and phase levels or element spacing functions of the array to get the desired characteristics of the radiation pattern. This paper addresses the problem of designing thinned linear arrays with practical radiating elements dipoles to achieve lowest side lobe level. Traditional methods are not well suited for thinning large arrays; hence Genetic algorithm(GA) is used to find the optimum thinned configuration to achieve the specified sidelobe level .The computed patterns are compared with the uniform array patterns for different number of elements.

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Section: Element Pattern Of Half Wave Dipolementioning

confidence: 89%

“…In case of large array with 'P' number of elements, the possible thinning combinations are 2 P [6]. So, checking all the combinations to find the optimum configuration is very laborious.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Thinned Dipole Arrays Using Genetic Algorithm

Lakshmi¹

2011

IJET

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“…It is unclear whether they can be easily extended to the multiple-pattern case since the best element positions usually change with different patterns. Some stochastic optimization algorithms capable of finding the global optimal solutions, such as in [26][27][28][29], may be appropriate, but they can be time-consuming since many unknowns including positions, multiple sets of excitations and even the number of elements, need to be determined. Recently, the extended forward-backward matrix pencil method (FBMPM) has been applied to find the common element positions with optimized excitations for multiple desired patterns [15].…”

Section: Introductionmentioning

confidence: 99%

Synthesis of Sparse or Thinned Linear and Planar Arrays Generating Reconfigurable Multiple Real Patterns by Iterative Linear Programming

You¹,

Zhu

Tan³

et al. 2016

PIER

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Abstract-It is shown in this paper that the problem of reducing the number of elements for multiplepattern arrays can be solved by a sequence of reweighted 1 optimizations under multiple linear constraints. To do so, conjugate symmetric excitations are assumed so that the upper and lower bounds for each pattern can be formulated as linear inequality constraints. In addition, we introduce an auxiliary variable for each element to define the common upper bound of both the real and imaginary parts of multiple excitations for different patterns, so that only linear inequality constraints are required. The objective function minimizes the reweighted 1 -norm of these auxiliary variables for all elements. Thus, the proposed method can be efficiently implemented by the iterative linear programming. For multiple desired patterns, the proposed method can select the common elements with multiple set of optimized amplitudes and phases, consequently reducing the number of elements. The radiation characteristics for each pattern, such as the mainlobe shape, response ripple, sidelobe level and nulling region, can be accurately controlled. Several synthesis examples for linear array, rectangular/triangular-grid and randomly spaced planar arrays are presented to validate the effectiveness of the proposed method in the reduction of the number of elements.

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“…Sparse arrays are facing nonlinear and complicate problems, and traditional numerical computation methods can not get the desirable solution. So more and more artificial intelligence methods are introduced in the field, such as dynamic programming algorithm [9], simulated annealing algorithm [2,[6][7][8]10], particle swarm algorithm [5], genetic algorithm [1,3,4,11], etc. Compared with linear and planar arrays, conformal cylindrical arrays have curved structure and are conformal to the carrier, so the synthesis problem of this type of antenna array is multidimensional and nonlinear problem, which is more difficult than the synthesis of linear and planar array [5].…”

Section: Introductionmentioning

confidence: 99%

Synthesis of Sparse Cylindrical Arrays Using Simulated Annealing Algorithm

Xie¹,

Ke-song²,

He³

2009

PIER Letters

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Abstract-In this paper, simulated annealing algorithm (SA) is applied to the synthesis of cylindrical conformal arrays in order to suppress the peaks of side lobes by acting on the elements' positions. There are multiple optimization constraints including the number of elements, aperture and minimum element spacing. A constraint matrix is designed to make the solution meet the restriction on the minima distance between elements, and the individual matrix which forms on the basis of constraint matrix is used to express array configurations. The SA does not act on the elements' positions directly but on the variables in a smaller solution space, this indirect description method makes the SA more computationally efficient. The simulation results confirm the great efficiency and robustness of the proposed method.

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Thinned arrays using genetic algorithms

Cited by 162 publications

References 1 publication

Optimization of Thinned Dipole Arrays Using Genetic Algorithm

Optimization of Thinned Dipole Arrays Using Genetic Algorithm

Synthesis of Sparse or Thinned Linear and Planar Arrays Generating Reconfigurable Multiple Real Patterns by Iterative Linear Programming

Synthesis of Sparse Cylindrical Arrays Using Simulated Annealing Algorithm

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