Efficient Embedded Element Pattern Prediction via Machine Learning

A Case Study with Planar Non-Uniform Sub-Arrays

Conference paper (2023)

Authors

N.B. Onat Microwave Sensing, Signals & Systems - EEMCS
I. Roldan Montero Microwave Sensing, Signals & Systems - EEMCS
F. Fioranelli Microwave Sensing, Signals & Systems - EEMCS
Alexander Yarovoy Microwave Sensing, Signals & Systems - EEMCS
Y. Aslan Microwave Sensing, Signals & Systems - EEMCS
Research Group
Microwave Sensing, Signals & Systems (EEMCS) (TU Delft)
Copyright: © 2023 N.B. Onat, I. Roldan Montero, F. Fioranelli, Alexander Yarovoy, Y. Aslan
DOI
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https://doi.org/10.23919/EuCAP57121.2023.10133770
Irregular antenna array Phased array antenna Artificial neural network (ANN) Embedded element pattern (EEP) Mutual coupling (MC)
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Published Date

2023

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Microelectronics

Research Group

Microwave Sensing, Signals & Systems

Abstract

Efficient prediction of embedded element patterns (EEPs) is including the mutual coupling (MC) effects in the optimization of irregular planar arrays is studied for the first time in the literature. An ANN-based methodology is used to predict the pattern of each element in the whole visible space for a flexible planar array topology in milliseconds. The technique is proposed is validated on a 4-element planar non-uniform sub-array structure. Excellent accuracy on the EEP prediction while providing great efficiency in computational time and load in comparison to the full-wave simulations is demonstrated.