A sidelobe level reduction (SLL) for planar array antennas with −30 dB attenuators weight precision

Aboul‐Seoud, A.; Mahmoud, Ahmed; Hafez, Alaa El-Din Sayed

doi:10.1016/j.ast.2010.02.005

Cited by 12 publications

(6 citation statements)

References 13 publications

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“…To attain the lowest null and enable a fair comparison with other algorithms, the population size of the NCGWO algorithm was set to 30, and the maximum number of iterations was set to 1000. Figure 15a illustrates the sparse LAA pattern using the NCGWO algorithm and compares it with the results of the GWO, IGWO, WDO, and ACO algorithms [39,40]. Figure 15b displays the iterative curves of GWO, IGWO, and NCGWO.…”

Section: Ten-element Sparse Laa With Constraint On the Minimum Elemen...mentioning

confidence: 99%

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Optimal Pattern Synthesis of Linear Array Antennas Using the Nonlinear Chaotic Grey Wolf Algorithm

Zhao,

Liu,

2023

Electronics

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The grey wolf optimization (GWO) algorithm is a new nature-inspired meta-heuristic algorithm inspired by the social hierarchy and hunting behavior of grey wolves. In this paper, the GWO algorithm is improved to overcome previous shortcomings of being easily trapped in local optima and having a low convergence rate. The proposed enhancement of the GWO algorithm utilizes logistic-tent double mapping to generate initialized populations, which enhances its global search capability and convergence rate. This improvement is called the nonlinear chaotic grey wolf optimization (NCGWO) algorithm. The performance of the NCGWO algorithm was evaluated with four representative benchmark functions. Then, the NCGWO algorithm was applied to perform an optimal pattern synthesis of linear array antennas (LAAs) using two distinct approaches: optimizing the amplitudes of the antenna currents while preserving uniform spacing and optimizing the positions of the antennas while assuming uniform excitation. To validate the effectiveness of the proposed approach, the results obtained by the NCGWO algorithm were compared with those obtained by other intelligent algorithms. Additionally, the NCGWO algorithm was applied to a more complex planar antenna array to further validate its performance. Our results demonstrate that the NCGWO algorithm exhibits superior performance regarding electromagnetic optimization problems compared to widely recognized algorithms.

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Section: Ten-element Sparse Laa With Constraint On the Minimum Elemen...mentioning

confidence: 99%

“…Figure 15a illustrates the sparse LAA pattern using the NCGWO algorithm and compares it with the results of the GWO, IGWO, WDO, and ACO algorithms [39,40]. Figure 15b displays the iterative curves of GWO, IGWO, and NCGWO.…”

Section: Ten-element Sparse Laa With Constraint On the Minimum Elemen...mentioning

confidence: 99%

Optimal Pattern Synthesis of Linear Array Antennas Using the Nonlinear Chaotic Grey Wolf Algorithm

Zhao,

Liu,

2023

Electronics

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“…If the direction of interference is not known, the target is to reduce the maximum potential interference that is often caused by the beam pattern sidelobes. Traditional sidelobe reduction techniques discussed in [20]- [23] require amplitude control per antenna element to window the power radiated by antenna array. Windowing softens the edge effect, which is the main reason for the sidelobes.…”

Section: A Unknown Direction Of Interferencementioning

confidence: 99%

Spatial Interference Reduction by Subarray Stacking in Large Two-Dimensional Antenna Arrays

Javed

Tervo

Leinonen

et al. 2021

IEEE Trans. Antennas Propagat.

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“…Basically the antenna array design involves calculating the complex currents of the individual antenna elements and selecting an appropriate antenna element. The current excitations would largely determine how sharp the resultant radiation pattern is and how small the sidelobe levels are in comparison to the main lobe [2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Synthesis of a Linear Antenna Array for Maximum Side-lobe Level Reduction

Babale¹,

Dajab²,

Ahmad³

2014

IJCA

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There are number of Techniques used to reduce Side Lobe Level (SLL) of antenna arrays to save power and improve Quality of Service (QoS) by ensuring maximum radiation in desired direction, several methods are available in literature. It is a well known fact that when an attempt is made to reduce the first sidelobe level of an antenna array, the beamwidth increases. However, in the present work, an array for a specified first side lobe level of -35dB was synthesized using Dolph Chebyshev method. A real-value Genetic Algorithm (RGA) was also used to optimize the current excitations. The weighting vectors are compared. Using these vectors, patterns are generated for arrays of different elements. The resultant patterns are compared and the result shows greater improvement in the SLL reduction from the RGA method without deteriorating the main beamwidth.

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A sidelobe level reduction (SLL) for planar array antennas with −30 dB attenuators weight precision

Cited by 12 publications

References 13 publications

Optimal Pattern Synthesis of Linear Array Antennas Using the Nonlinear Chaotic Grey Wolf Algorithm

Optimal Pattern Synthesis of Linear Array Antennas Using the Nonlinear Chaotic Grey Wolf Algorithm

Spatial Interference Reduction by Subarray Stacking in Large Two-Dimensional Antenna Arrays

Synthesis of a Linear Antenna Array for Maximum Side-lobe Level Reduction

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