Performance assessment of sequential Bayesian processors based on probably approximately correct computation and information theory

• Published in: Electronics letters Vol. 54; no. 6; pp. 357 - 359
• Main Authors: Jaras, I, Orchard, M.E
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 22.03.2018
• Subjects: Bayesian estimation algorithms; Information and communications; information theory; Kalman filters; lithium‐ion battery state‐of‐charge monitoring; nonlinear filters; performance assessment; sequential Bayesian processors; state estimation; unscented Kalman filter; lithium-ion battery state-of-charge monitoring; Bayesian estimation algorithms; state estimation; performance assessment; nonlinear filters; information theory; Kalman filters; sequential Bayesian processors; unscented Kalman filter
• ISSN: 0013-5194; 1350-911X; 1350-911X
• DOI: 10.1049/el.2017.4159

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.