Industrial process data are usually mixed with missing data and outliers which can greatly
affect the statistical explanation abilities for traditional data-driven modeling methods. In this …

The word robust has been used in many contexts in signal processing. Our treatment
concerns statistical robustness, which deals with deviations from the distributional …

This paper develops a robust iterated extended Kalman filter (EKF) based on the
generalized maximum likelihood approach (termed GM-IEKF) for estimating power system …

The unscented Kalman filter (UKF) has proved to be a promising methodology to integrate
INS and GPS for vehicular navigation. Nevertheless, the disturbance suppression of system …

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 …

This paper is concerned with the distributed recursive filtering problem for a class of discrete
time-delayed stochastic systems subject to both uniform quantization and deception attack …

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 is concerned with the ultimately bounded filtering problem for a class of linear
time-delay systems subject to norm-bounded disturbances and impulsive measurement …

This article is concerned with the partial-node-based (PNB) state estimation problem for
delayed complex networks (DCNs) subject to intermittent measurement outliers (IMOs). In …

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 …