Directivity Estimations for Short Dipole Antenna Arrays Using Radial Basis Function Neural Networks


The role of directivity is very vital within the operation of an array as it offers a measure of the effectiveness of the array in pointing the radiations during a specific direction. Ancient strategies used for the computation of directivity are though effective but might be time consuming. Artificial neural networks (ANNs) don't require the advanced mathematical procedures and are thus faster. Being nonlinear in nature, ANNs adapt to the nonlinear behavior of antenna arrays simply. In this letter, directivity estimations for the uniform linear arrays of collinear short dipoles and parallel short dipoles, using radial basis function neural networks (RBF-NNs) are presented. The algorithm has also been applied for a planar array with short dipoles. The robustness of the method has been tested by evaluating its performance for noisy knowledge conditions. The highlight options of the study are the accuracy and speed shown by the strategy in estimating results for the unseen inputs even in noisy data conditions.

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