Logistics is an important driver for the competitiveness of industries and material supply. The
development of smart logistics, powered by precise positioning and communication …

The goal of the interval observers is to deal with the large but bounded uncertainties and
disturbances by determining certain interval (upper and lower estimates) for the system …

Abstract Along with Markov chain Monte Carlo (MCMC) methods, variational inference (VI)
has emerged as a central computational approach to large-scale Bayesian inference …

Now in its second edition, this accessible text presents a unified Bayesian treatment of state-
of-the-art filtering, smoothing, and parameter estimation algorithms for non-linear state …

In this paper, we extend the cubature Kalman filter (CKF) to deal with nonlinear state-space
models of the continuous-discrete kind. To be consistent with the literature, the resulting …

In this paper, we propose a systematization of the (discrete-time) Unscented Kalman Filter
(UKF) theory. We gather all available UKF variants in the literature, present corrections to …

In this brief, to get a more accurate and robust state of charge (SoC) estimation, the lithium-
ion battery model parameters are identified using an adaptive unscented Kalman filtering …

A new estimation method for power system dynamic state estimation, the unscented Kalman
filter (UKF), is presented. It is based on the application of the unscented transformation (UT) …

Wasserstein gradient flows provide a powerful means of understanding and solving many
diffusion equations. Specifically, Fokker-Planck equations, which model the diffusion of …

This note considers the application of the unscented transform to optimal smoothing of
nonlinear state--space models. In this note, a new Rauch--Tung--Striebel type form of the …