The Kalman filter (KF) is a popular state estimation technique that is utilized in a variety of
applications, including positioning and navigation, sensor networks, battery management …

In recent years, unmanned aircraft systems (UASs) have played an increasingly significant
role in the military and civil fields. The flight control system, as the “hub” of an unmanned …

Advanced driver-assistance systems (ADASs) have become a salient feature for safety in
modern vehicles. They are also a key underlying technology in emerging autonomous …

In this paper, a novel Gaussian-Student's t mixture (GSTM) distribution is proposed to model
non-stationary heavy-tailed noises. The proposed GSTM distribution can be formulated as a …

Abstract State-of-charge (SOC) estimation is an important aspect for modern battery
management systems. Extended Kalman filter (EKF) has been extensively used in battery …

To date, most linear and nonlinear Kalman filters (KFs) have been developed under the
Gaussian assumption and the well-known minimum mean square error (MMSE) criterion. In …

This article proposes a robust Bayesian inference approach for linear state-space models
with nonstationary and heavy-tailed noise for robust state estimation. The predicted …

Remaining useful life (RUL) prediction is a vital task in rolling bearing prognostics and
health management (PHM) process. Kalman filtering (KF) is one of the hot spots in the …

In this article, a statistical similarity measure is introduced to quantify the similarity between
two random vectors. The measure is, then, employed to develop a novel outlier-robust …

In this paper, a new robust Kalman filtering framework for a linear system with non-Gaussian
heavy-tailed and/or skewed state and measurement noises is proposed through modeling …