In this article, the adaptive online state smoothing problem is studied for a Markov jump
system where the measurement noise covariance matrix (MNCM) is unknown. To address …

In this work, we investigate the problem of state estimation for a class of nonlinear systems
subjected to randomly occurring measurement anomalies (ROMAs) without a priori statistic …

In this study, we introduce a novel residual-based Bayesian expectation–maximization
adaptive Kalman filter (RBEMAKF) for dynamic state estimation with inaccurate and time …

This paper investigates the state estimation problem for a class of stochastic system under
randomly occurring measurement anomalies, and the Kalman filter is combined with …

Cellular signals have become a promising candidate for localization due to advantages
such as broad coverage and high signal quality, especially in dense urban environments …

In this article, we consider the Kalman filtering problem in the presence of non-Gaussian
measurement noise contaminated by time-correlated outliers. A novel robust filtering …

This paper introduces a novel robust Kalman filter designed to leverage symmetrical
properties within the Pearson Type VII-Inverse Wishart (PVIW) distribution, enhancing state …

In this article, we consider the problem of outlier-robust state estimation where the
measurement noise can be correlated. Outliers in data arise due to many reasons like …

In many practical fields, the unknown time-varying measurement biases (additive and
multiplicative bias) and heavy-tailed measurement noise caused by some unpredictable …

An adaptive target tracking method based on marginalized cubature Kalman filter is
proposed for the target tracking problem in the presence of sensor measurement biases and …