Neural Network Modeling for the Reduction of Scattering Grating Lobes of Arrays
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https://doi.org/10.13052/2023.ACES.J.380901关键词:
Artificial neural network, metal wall, radar cross-section, scattering grating lobe摘要
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.
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