학술논문

Bayesian Active Learning for Radiation Pattern Sampling Over Cylindrical Surfaces
Document Type
Periodical
Source
IEEE Transactions on Electromagnetic Compatibility IEEE Trans. Electromagn. Compat. Electromagnetic Compatibility, IEEE Transactions on. 64(5):1391-1398 Oct, 2022
Subject
Fields, Waves and Electromagnetics
Engineered Materials, Dielectrics and Plasmas
Kernel
Antenna measurements
Bayes methods
Surface treatment
Position measurement
Computational modeling
Antenna radiation patterns
Active learning
Bayesian optimization (BO)
design of experiments (DoE)
electromagnetic (EM) compatibility
radiation pattern
Language
ISSN
0018-9375
1558-187X
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.