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 …

Communication delay and non-Gaussian noise are challenging issues for underwater
navigation and positioning. This study proposes a filtering algorithm for strapdown inertial …

Particle Filters (PFs) are commonly used Sequential Monte Carlo (SMC) algorithms to
process a never-ending stream of measurements relating to a nonlinear non-Gaussian state …

In this paper, an improved unscented Kalman filter is presented to achieve state estimation
for nonlinear systems with one-step randomly delayed measurement and unknown latency …

At least four non-coplanar anchor nodes (ANs) are required for the time-of-arrival (ToA)-
based three-dimensional (3D) positioning to enable unique position estimation. Direct …

Conventional Bayesian estimation requires an accurate stochastic model of a system.
However, this requirement is not always met in many practical cases where the system is not …

The simultaneous presence of uncertain data delays and data loss in a network control
system complicates the state estimation problem and its solution. This paper redesigns the …

Abstract Sequential Monte Carlo (SMC) methods are a well-established family of Bayesian
inference algorithms for performing state estimation for Non-Linear Non-Gaussian models …

A networked system often uses a shared communication network to transmit the
measurements to a remotely located estimation center. Due to the limited bandwidth of the …

This article addresses the state estimation problem for dynamic systems with linear models
wherein covariance matrices of the process and measurement noise are unknown and one …