This paper points out the flaws in using the extended Kalman filter (EKE) and introduces an
improvement, the unscented Kalman filter (UKF), proposed by Julier and Uhlman (1997). A …

Probabilistic inference is the problem of estimating the hidden variables (states or
parameters) of a system in an optimal and consistent fashion as a set of noisy or incomplete …

The estimation of a vehiclepsilas dynamic state is one of the most fundamental data fusion
tasks for intelligent traffic applications. For that, motion models are applied in order to …

Solutions for indoor tracking and localization have become more critical with recent
advancement in context and location-aware technologies. The accuracy of explicit …

Battery management system (BMS) requires an accurate prediction the remaining energy
level or state of charge (SOC) of the cell or battery pack. However, in electric vehicles …

This book examines Gaussian processes in both model-based reinforcement learning (RL)
and inference in nonlinear dynamic systems. First, we introduce PILCO, a fully Bayesian …

The Kalman filter is a well-known recursive state estimator for linear systems. In practice, the
algorithm is often used for non-linear systems by linearizing the system's process and …