Open AccessAdaptive frequency sampling using linear Bayesian vector fitting
Author(s) -
De Ridder S.,
Deschrijver D.,
Spina D.,
Dhaene T.,
Vande Ginste D.
Publication year - 2019
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2018.6668
Subject(s) - construct (python library) , range (aeronautics) , bayesian probability , measure (data warehouse) , computer science , antenna (radio) , sampling (signal processing) , algorithm , electronic engineering , data mining , artificial intelligence , telecommunications , engineering , detector , programming language , aerospace engineering
The authors present a novel Bayesian approach to adaptively select frequency samples to obtain a rational macromodel of device responses over a broad frequency range while performing as few electromagnetic simulations as possible. The method leverages a Bayesian approach to vector fitting to construct a data‐driven uncertainty measure. The presented technique is demonstrated by application to a double semi‐circular patch antenna and is shown to accurately and efficiently construct a rational macromodel over the frequency range of interest.