Open AccessPerformance assessment of sequential Bayesian processors based on probably approximately correct computation and information theory
Author(s) -
Jaras I.,
Orchard M.E.
Publication year - 2018
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
ISSN - 1350-911X
DOI - 10.1049/el.2017.4159
Subject(s) - computation , kalman filter , particle filter , bayesian probability , computer science , context (archaeology) , recursive bayesian estimation , algorithm , extended kalman filter , state of charge , state (computer science) , bayesian programming , bayes estimator , battery (electricity) , artificial intelligence , bayesian inference , bayesian statistics , power (physics) , paleontology , physics , quantum mechanics , biology
A novel method to characterise the efficacy and efficiency of different sequential Bayesian processor implementations is proposed. This method is based on concepts of probably approximately correct computation and information theory measures. The proposed approach is used to compare the performance of three different Bayesian estimation algorithms (particle filter, unscented Kalman filter (UKF), and UKF with outer feedback correction loops) in the context of lithium‐ion battery state‐of‐charge monitoring.