A hybrid approach for the optimal synthesis of pencil beams through array antennas

Isernia, Tommaso; Ares, F.; Bucci, O.M.; D’Urso, M.; Gómez, Julio; Rodríguez, Juan Antonio

doi:10.1109/aps.2004.1331831

Cited by 13 publications

(16 citation statements)

References 20 publications

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“…1 and 2; we selected a set of specifications with variable beamwidth, number of elements and side lobe level, in order to provide satisfactory range of specifications to test sparse synthesis algorithm. It is worth underlining that these problems can be always solved by BEA with simple Dolph-Chebyshev excitation; to check the toughness of these problems we have also tried to solve them by means of a hybrid algorithm [9] and a compressive sensing inspired algorithm [7] to verify if it was possible to achieve the same results with a lower number of elements, but we were not able to find such better results.…”

Section: "Tough" Problems To Test Your Algorithmsmentioning

confidence: 99%

“…As first example of the application of the proposed benchmarking procedure, let us now consider the sparse array obtained in [9] by means of a hybrid synthesis method. For this pattern u s = 0, u 1 = 0.0399367, SLL = −20 dB and the number of employed elements is N = 25.…”

Section: First Examplementioning

confidence: 99%

“…Due to their advantages, several methods have been developed: evolutionary techniques [1][2][3][4], compressive sensing inspired approaches [5][6][7][8], and many others [9][10][11][12][13]. In spite of the importance of the problem, no specific comparison procedures among synthesis methods have been developed up to date.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparison Guidelines and Benchmark Procedure for Sparse Array Synthesis

Pinchera¹,

Migliore²

2016

PIER M

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Abstract-A benchmarking procedure for sparse linear array synthesis methods is proposed. Our approach is based on the comparison of the performance of the array synthesis algorithm under test with the performance of a reference solution based on a optimally equispaced array. The benchmark procedure is demonstrated considering some examples regarding sparse synthesis method proposed in literature. Guidelines for the correct comparison of synthesis methods and some "tough problems" for the test of new sparse synthesis algorithms are also provided.

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Section: "Tough" Problems To Test Your Algorithmsmentioning

confidence: 99%

Section: First Examplementioning

confidence: 99%

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Comparison Guidelines and Benchmark Procedure for Sparse Array Synthesis

Pinchera¹,

Migliore²

2016

PIER M

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“…Let us now consider the same constraints considered in [20]. The specifications of this pencil beam are unsymmetrical, |F(u)| ≤ −19.68 dB for −2 ≤ u ≤ −0.1236 and |F(u)| ≤ −29.54 dB for 0.1236 ≤ u ≤ 2, with |F(0)| = 1.…”

Section: Sparse Array Synthesis With Arbitrary Upper Masksmentioning

confidence: 99%

A Compressive-Sensing Inspired Alternate Projection Algorithm for Sparse Array Synthesis

et al. 2016

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Abstract:In this paper, we propose a simple approach for sparse array synthesis. We employ a modified generalized alternate projection algorithm using 1 -norm constrained minimization in order to achieve the excitation and the position of the elements of a sparse array. The proposed approach is very flexible, since it deals with power pattern masks and allows the inclusion of the effects of element pattern and mutual coupling. Its implementation is relatively simple, thanks to the possibility to use well-known convex programming techniques. The presented method is particularly suitable for the synthesis of patterns commonly employed in radar systems; the numerical results provided show good performances with respect to concurrent methods available in open literature.

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“…Three linear array design cases of 10, 16 and 24 elements are optimized using Taguchi's and SADE methods. It must be pointed out that the synthesis of excitations can be formulated as a Convex Programming problem or even as Linear Programming problem and solved more efficiently [24][25][26][27]. But, this case also represents an appropriate example to compare both Taguchi's and SADE methods.…”

Section: Optimize Element Amplitudes (I N )mentioning

confidence: 99%

Application of Taguchi's Optimization Method and Self-Adaptive Differential Evolution to the Synthesis of Linear Antenna Arrays

Dib¹,

Goudos²,

Muhsen³

2010

PIER

102

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Abstract-In this paper, the problem of designing linear antenna arrays for specific radiation properties is dealt with. The design problem is modeled as a single optimization problem. The objectives of this work are to minimize the maximum side lobe level (SLL) and perform null steering for isotropic linear antenna arrays by controlling different parameters of the array elements (position, amplitude, and phase). The optimization is performed using two techniques: Taguchi's optimization method and the self-adaptive differential evolution (SADE) technique. The advantage of Taguchi's optimization technique is the ability of solving problems with a high degree of complexity using a small number of experiments in the optimization process Taguchi's method is easy to implement and converges to the desired goal quickly in comparison with gradient-based methods and particle swarm optimization (PSO) Results obtained using Taguchi's method are in very good agreement with those obtained using the SADE technique.

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A hybrid approach for the optimal synthesis of pencil beams through array antennas

Cited by 13 publications

References 20 publications

Comparison Guidelines and Benchmark Procedure for Sparse Array Synthesis

Comparison Guidelines and Benchmark Procedure for Sparse Array Synthesis

A Compressive-Sensing Inspired Alternate Projection Algorithm for Sparse Array Synthesis

Application of Taguchi's Optimization Method and Self-Adaptive Differential Evolution to the Synthesis of Linear Antenna Arrays

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