This article deals with state estimation of nonlinear stochastic dynamic systems. The stress is
laid on general introduction of the selected estimation methods, description of their …

In this paper, a novel variational Bayesian (VB)-based adaptive Kalman filter (VBAKF) for
linear Gaussian state-space models with inaccurate process and measurement noise …

Since the landmark work of RE Kalman in the 1960s, considerable efforts have been
devoted to time series state space models for a large variety of dynamic estimation …

A novel robust Student's t-based Kalman filter is proposed by using the variational Bayesian
approach, which provides a Gaussian approximation to the posterior distribution. Simulation …

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 …

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 …

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 …

Novel Student's t based approaches for formulating a filter and smoother, which utilize heavy
tailed process and measurement noise models, are found through approximations of the …

From the celebrated Gaussian mixture, model averaging estimators to the cutting-edge multi-
Bernoulli mixture of various forms, finite mixture models offer a fundamental and flexible …