Bayesian Active Learning for Radiation Pattern Sampling Over Cylindrical Surfaces

Abstract

In this article, a new motion-aware sampling strategy (MASS) is presented to speed up the measurement of radiation patterns around cylindrical surfaces. Differently from preexisting sampling techniques, the MASS directly chooses positions that reduce the overall travel time of the field antenna, rather than minimizing the total number of samples. The proposed strategy employs a Gaussian process model that is adapted to the field over a cylindrical surface. Moreover, a new acquisition function for Bayesian active learning is developed in order to efficiently search the peaks of the measured field and predict their values. Next, the proposed strategy is tested on the experimental data from a radiation pattern of a comb generator. Finally, the results are compared to standard grid sampling and Bayesian optimization strategies.