Constrained Infinitesimal Dipole Modeling-Assisted Ensemble Prediction of Embedded Element Patterns via Machine Learning
N.B. Onat (TU Delft - Microwave Sensing, Signals & Systems)
Ignacio Roldan Montero (TU Delft - Microwave Sensing, Signals & Systems)
Francesco Fioranelli (TU Delft - Microwave Sensing, Signals & Systems)
A. G. Yarovyi (TU Delft - Microwave Sensing, Signals & Systems)
Y. Aslan (TU Delft - Microwave Sensing, Signals & Systems)
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Abstract
A novel ensemble prediction technique is introduced to enhance the accuracy of far-field embedded element pattern (EEP) prediction under mutual coupling (MC) effects, while relaxing the training data size challenge in neural network (NN)-based algorithms. The proposed method integrates a two-stage NN for direct EEP prediction from full-wave simulated pattern data in spherical coordinates with a fully connected NN for the prediction of excitation coefficients of an array of infinitesimal dipoles, approximating the full-wave simulated EEPs via constrained infinitesimal dipole modeling (IDM). Quasi-randomly distributed five-element pin-fed S-band patch antenna arrays are used for demonstration purpose. It is shown that, for a large-sized (3500 topologies) and relatively small-sized (1500 topologies) dataset, incorporating IDM-NN with the benchmarked direct EEP-NN in an ensemble technique increases the pattern prediction accuracy by 11% and 60% on average, respectively.