Abstract

The monostatic radar cross-section (RCS) of an array is seriously deteriorated by the scattering grating lobe. In this paper, the scattering grating lobe of an array is suppressed by metal walls around elements. The artificial neural network with Fourier series-based transfer functions is used to accelerate the design process. A 1×8 array with the patch element operating in the range from 9.4 to 10.6 GHz is studied. The monostatic RCS of the array with designed metal walls is compared with that of the array with no metal wall. Simulated results show that the scattering grating lobe of the array with metal walls is suppressed by 5.8 dB at 12 GHz, and the change of radiation performance is acceptable. The design procedure is also available for other arrays with reduced scattering grating lobes.

Details

Title
Neural Network Modeling for the Reduction of Scattering Grating Lobes of Arrays
Author
Liu, Zhi-Xian; Wen-Hao, Su; Sheng-Jun, Zhang; Shao, Wei
Pages
633-637
Section
Special Issue on ACES-China 2022 Conference
Publication year
2023
Publication date
2023
Publisher
River Publishers
ISSN
10544887
e-ISSN
19435711
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2933896124
Copyright
© 2023. This work is published under https://creativecommons.org/licenses/by-nc/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.