The nonlinear filtering problem has enduringly been an active research topic in both
academia and industry due to its ever-growing theoretical importance and practical …

This article addresses the Gaussian filtering problem under the environment of jointly
occurring delayed and missing measurements. In this work, the former irregularity is …

Gaussian filtering traditionally suffers from two major drawbacks: 1) Gaussian approximation
of the intrinsic non-Gaussian measurement noises and 2) ignoring delay in measurements …

Gaussian filtering is a commonly used nonlinear filtering method. This letter proposes an
advanced Gaussian filtering method for handling cyber-attacked measurement data. It …

State estimation in cyber-physical systems is a challenging task involving integrating
physical models and measurements to estimate dynamic states accurately in practical …

Particle filtering is probably the most widely accepted methodology for general nonlinear
filtering applications. The performance of a particle filter critically depends on the choice of …

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

The Van-der Pol oscillator is used to analyze realistic oscillations in a variety of real-world
systems. Among the most famous self-oscillating systems is the Van-der Pol oscillator. Its …

Due to interference in the transmission of external equipment, images can suffer from
varying noise concentrations. As an immediate and practical step to reduce the impact of …

Gaussian filtering is a popular Bayesian approximation filtering method for nonlinear
systems. In general, the Gaussian filters are designed for non-delayed measurements; …