Low Sidelobe Cosecant-Squared Pattern Synthesis for Large Planar Array Using Genetic Algorithm

Sallam, Tarek; Attiya, Ahmed M.

doi:10.2528/pierm20042005

Cited by 15 publications

(10 citation statements)

References 27 publications

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“…This demonstrates that the CNN model succeeds in realizing the adaptive beamforming of PARs in real time. Furthermore, the method can be easily extended to other synthesis methods such amplitude- and phase-only syntheses (Sallam and Attiya, 2019).…”

Section: Discussionmentioning

confidence: 99%

Convolutional neural network for fast adaptive beamforming of phased array weather radar

Sallam

2023

COMPEL

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Purpose The purpose of this paper is to present a deep-learning-based beamforming method for phased array weather radars, especially whose antenna arrays are equipped with large number of elements, for fast and accurate detection of weather observations. Design/methodology/approach The beamforming weights are computed by a convolutional neural network (CNN), which is trained with input–output pairs obtained from the Wiener solution. Findings To validate the robustness of the CNN-based beamformer, it is compared with the traditional beamforming methods, namely, Fourier (FR) beamforming and Capon beamforming. Moreover, the CNN is compared with a radial basis function neural network (RBFNN) which is a shallow type of neural network. It is shown that the CNN method has an excellent performance in radar signal simulations compared to the other methods. In addition to simulations, the robustness of the CNN beamformer is further validated by using real weather data collected by the phased array radar at Osaka University (PAR@OU) and compared to, besides the FR and RBFNN methods, the minimum mean square error beamforming method. It is shown that the CNN has the ability to rapidly and accurately detect the reflectivity of the PAR@OU with even less clutter level in comparison to the other methods. Originality/value Motivated by the inherit advantages of the CNN, this paper proposes the development of a CNN-based approach to the beamforming of PAR using both simulated and real data. In this paper, the CNN is trained on the optimum weights of Wiener solution. In simulations, it is applied on a large 32 × 32 planar phased array antenna. Moreover, it is operated on real data collected by the PAR@OU.

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Section: Discussionmentioning

confidence: 99%

Convolutional neural network for fast adaptive beamforming of phased array weather radar

Sallam

2023

COMPEL

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“…Numerous researchers have created methods for dealing with planar antenna array (PAA) problems that use amplitude excitation, phase, and array element spacing [3][4][5][6][7][8][9][10][11][12][13][14]. For narrow PAAs with isotropic components, the literature [3][4][5][6] give techniques for reducing side lobe level, starting null beam width, and producing radiation patterns at different scan angles.…”

Section: Introductionmentioning

confidence: 99%

Small planar antenna array design using length and spacing through Matlab‐HFSS interfacing

Patidar,

Das,

Kar

2024

Int J Communication

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SummaryThis study presents a novel method for reducing return loss and achieving lower side lobe levels in small planar antenna arrays (PAAs). Quantum particle swarm optimisation (QPSO) findings are utilised for antenna design modelling and performance assessment utilising the MATLAB‐based moment's code approach and MATLAB‐HFSS interface. The mutual coupling between antenna components is considered and computed using the method of moments (MoM). Changing the array elements' length and spacing will accomplish the abovementioned objectives. The Matlab‐based moments code calculates the antenna components' length and spacing, and the antennas' length and spacing are used to produce the radiation pattern via the Matlab‐HFSS interface. Four examples of small PAA designs are given to illustrate how well the applied methodology works. The method discussed in this paper is generic and can be employed in the future for different sizes of the array elements and the different antenna array structure designs.

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“…In order to generate a specific radiation pattern, different algorithms have been used to synthesize and optimize the phase distribution on the reflectarray elements. The optimization methods used to synthesis the reflectarray pattern are classified into two main categories: local search algorithms, such as the alternating projections method [ 15 , 16 ], and evolutionary algorithms, such as GA [ 17 , 18 , 19 , 20 ], particle swarm optimization (PSO) [ 21 ], and the semidefinite relaxation technique [ 22 ]. These different optimization algorithms vary in terms of their computation complexity and final convergence rate.…”

Section: Introductionmentioning

confidence: 99%

Analysis and Design of an X-Band Reflectarray Antenna for Remote Sensing Satellite System

Soliman

Eldesouki

Attiya

2022

Sensors

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This paper presents the analysis and design of an X-band reflectarray. The proposed antenna can be used for a medium Earth orbit (MEO) remote sensing satellite system in the 8.5 GHz band. To obtain a nearly constant response along the coverage area of this satellite system, the proposed antenna was designed with a flat-top radiation pattern with a beam width of around 29° for the required MEO system. In addition, broadside pencil beam and tilted pencil beam reflectarrays were also investigated. The feeding element of the proposed reflectarray antennas is a Yagi–Uda array. The amplitude and phase distribution of the fields due to the feeding element on the aperture of the reflectarray antenna are obtained directly by numerical simulation without introducing any approximation. The required phase distribution along the aperture of the reflectarray to obtain the required flat-top radiation pattern is obtained using the genetic algorithm (GA) optimization method. The reflecting elements of the reflectarray are composed of stacked circular patches. This stacked configuration was found to be appropriate for obtaining a wide range of reflection phase shift, which is required to implement the required phase distribution on the reflectarray aperture. The antenna was fabricated and measured for verification.

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Low Sidelobe Cosecant-Squared Pattern Synthesis for Large Planar Array Using Genetic Algorithm

Cited by 15 publications

References 27 publications

Convolutional neural network for fast adaptive beamforming of phased array weather radar

Convolutional neural network for fast adaptive beamforming of phased array weather radar

Small planar antenna array design using length and spacing through Matlab‐HFSS interfacing

Analysis and Design of an X-Band Reflectarray Antenna for Remote Sensing Satellite System

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