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

Abstract

The role of directivity is very important in the operation of an array as it gives a measure of the effectiveness of the array in pointing the radiations in a specific direction. Traditional methods used for the computation of directivity are although effective but may be time consuming. Artificial neural networks (ANNs) do not require the complex mathematical procedures and are therefore faster. Being nonlinear in nature, ANNs adapt to the nonlinear behavior of antenna arrays easily. 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) have been 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 data conditions. The highlight features of the study are the accuracy and speed shown by the method in estimating results for the unseen inputs even in noisy data conditions.