Statistical analysis of phased arrays with random excitation errors using a unified neural network architecture

Abstract

AbstractRadiation patterns of a phased array tend to be affected by random excitation errors. In this paper, a unified neural network (UNN) architecture is presented to study the statistical characteristics of radiation patterns. Benefited from the inherent symmetry of the array manifolds and efficient data preprocessing, the UNN architectures are almost identical for planar arrays consisting of 4–10 000 elements except for a slight difference existed in the output layer. Therefore, this merit avoids repetitive selections of training hyperparameters and network architectures. This method can efficiently treat the problems with multiple observation points in order to obtain statistical radiation patterns, and an analytic or closed‐form solution is not required. Moreover, it can combine with the method of moments (MoM), which takes account of the element pattern and mutual coupling effects. The trained UNN models can predict the probability density function (PDF) of radiation characteristics at any spatial location. Numerical results show that the UNN and Monte Carlo (MC) results are comparable in accuracy.