Synthesis of Sparse Planar Arrays Using Modified Real Genetic Algorithm

Ke-song, Chen; Yun, Xiaohua; He, Zhiming; Han, Chuang

doi:10.1109/tap.2007.893375

Cited by 210 publications

(207 citation statements)

References 13 publications

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“…For Equation (3), the fitness function is the sum of the maximum PSLL in φ = 0 • and φ = 90 • planes, that can be expressed as [26]:…”

Section: Fitness Functionmentioning

confidence: 99%

Synthesis of Thinned Linear and Planar Antenna Arrays Using Binary Pso Algorithm

Wang¹,

Feng²,

Liu³

2012

PIER

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Abstract-Traditional optimization methods are not well suitable for thinning large arrays to obtain a low sidelobe level (SLL). The chaotic binary particle swarm optimization (CBPSO) algorithm is presented as a useful alternative for the synthesis of thinned arrays. The proposed algorithm can be improved by nonlinear inertia weight with chaotic mutation to increase the diversity of particles. Two examples have been presented and solved. Simulation results are proposed to compare with published results to verify the effectiveness of the proposed method for both linear and planar arrays.

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“…For Equation (3), the fitness function is the sum of the maximum PSLL in φ = 0 • and φ = 90 • planes, that can be expressed as [26]:…”

Section: Fitness Functionmentioning

confidence: 99%

Synthesis of Thinned Linear and Planar Antenna Arrays Using Binary Pso Algorithm

Wang¹,

Feng²,

Liu³

2012

PIER

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“…As for the synthesis of sparse planar arrays, some methods have been proposed in the literatures [2][3][4][5]. Some optimization algorithms have been successfully applied to optimize the element positions in order to achieve a better peak side lobe level performance [2][3].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Method for the Synthesis of Sparse Planar Antenna Arrays

Shen¹,

Wang²

2017

dtcse

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Abstract. Two-dimensional unitary matrix pencil (2-D UMP) method is utilized for the synthesis of sparse planar arrays in this paper. By virtue of the equivalent matrix pencil obtained with a unitary transformation, 2-D positions including horizontal and vertical coordinates are directly found as the generalized eigenvalues of the real matrix pencils without solving poles. Owing to the utilization of a unitary transformation, the computational complexity can be significantly reduced since only the real computations are involved in the singular value decomposition (SVD) and eigenvalue decomposition (EVD) procedures. A representative numerical experiment is provided to assess the effectiveness and advantages of the proposed method.

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“…For example, it has higher peak side lobe level [1,[3][4][5]. So how to suppress the peak side lobe level is an important research problem in sparse array design.…”

Section: Introductionmentioning

confidence: 99%

“…Thinning arrays is formed by removing some elements from uniform arrays, and the element spacing is integral times of original uniform arrays. The elements of sparse arrays are randomly distributed on the array surface, and the element spacing is indivisible [1,4]. By contrast, sparse arrays have more freedom than thinning arrays.…”

Section: Introductionmentioning

confidence: 99%

“…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%

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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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Synthesis of Sparse Planar Arrays Using Modified Real Genetic Algorithm

Cited by 210 publications

References 13 publications

Synthesis of Thinned Linear and Planar Antenna Arrays Using Binary Pso Algorithm

Synthesis of Thinned Linear and Planar Antenna Arrays Using Binary Pso Algorithm

An Efficient Method for the Synthesis of Sparse Planar Antenna Arrays

Synthesis of Sparse Cylindrical Arrays Using Simulated Annealing Algorithm

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