Gaussian Process Kernels for Efficient Sequential Sampling of Electromagnetic Radiation on Spherical Surfaces

Traditional far-field characterization of wireless devices relies on dense, fixed-grid measurements over the entire measurement surface, leading to high time and cost overheads. This paper develops a data-efficient sequential sampling strategy for far-field radiation measurements using Bayesian optimization (BO) with Gaussian Process (GP) surrogate modeling on spherical domains. A central contribution is the adaptation and evaluation of GP kernels to capture the continuity and smoothness of spherical radiation patterns, enabling accurate surrogate models for BO. To demonstrate practicality, we compare kernel choices within the proposed framework and introduce a motion-aware acquisition function that reduces travel time between sampling points. The methodology is validated on both simple and more directive antenna scenarios, showing that the proposed approach reliably identifies key radiation features with significantly fewer samples than dense grid-based methods. These results highlight the potential of BO-driven sequential sampling for efficient and reliable radiation characterization on spherical domains.