Sampling Method for Planar Near-field to Far-field Transformation using Gaussian Process Regression

The near-field to far-field transformation method is a well-established measurement technique that achieves high-accuracy far-field antenna radiation patterns from near-field data. However, this method is often time-consuming. This paper proposes an efficient sampling method using Gaussian process regression to calculate far-field data. By employing 86% undersampled near-field data with a sampling interval exceeding the limits set by the sampling theorem, accurate far-field radiation patterns can be derived through a limited number of Gaussian process regression iterations.