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 …

In this article, a novel variational Bayesian (VB) adaptive Kalman filter with inaccurate
nominal process and measurement noise covariances (PMNC) in the presence of outliers is …

If the system model or the statistical characteristics of noise are inaccurate, the past
measurements will directly affect the accuracy of current state estimation or even lead to …

In this article, a new variational adaptive Kalman filter with Gaussian-inverse-Wishart mixture
distribution is proposed for a class of linear systems with both partially unknown state and …

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 …

A slide window variational adaptive Kalman filter is presented in this brief based on adaptive
learning of inaccurate state and measurement noise covariance matrices, which is …

This paper presents a modified strong tracking unscented Kalman filter (MSTUKF) to
address the performance degradation and divergence of the unscented Kalman filter …

This paper proposes a modified unscented Kalman filter (UKF) with both adaptivity and
robustness. In the proposed filter, the adaptivity is achieved by estimating the time-varying …

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 …

The unscented Kalman filter (UKF) is a promising approach for the state estimation of
nonlinear dynamic systems due to its simple calculation process and superior performance …