Increasingly, for many application areas, it is becoming important to include elements of
nonlinearity and non-Gaussianity in order to model accurately the underlying dynamics of a …

In this paper, an extensive review for objects and drones (AUVs) detection and tracking is
presented. The article presents state of the art methods used in detection and tracking of …

Fault detection, isolation, and reconfiguration (FDIR) is an important and challenging
problem in many engineering applications and continues to be an active area of research in …

Hidden Markov models—most often abbreviated to the acronym “HMMs”—are one of the
most successful statistical modelling ideas that have came up in the last forty years: the use …

The particle filter (PF) was introduced in 1993 as a numerical approximation to the nonlinear
Bayesian filtering problem, and there is today a rather mature theory as well as a number of …

Modelling sequential data is important in many areas of science and engineering. Hidden
Markov models (HMMs) and Kalman filter models (KFMs) are popular for this because they …

In this paper, the distributed set-membership filtering problem is dealt with for a class of time-
varying multirate systems in sensor networks with the communication protocol. For relieving …

A framework for positioning, navigation, and tracking problems using particle filters
(sequential Monte Carlo methods) is developed. It consists of a class of motion models and …

In this self-contained survey/review paper, we systematically investigate the roots of
Bayesian filtering as well as its rich leaves in the literature. Stochastic filtering theory is …

Particle filtering in high dimensional state-spaces can be inefficient because a large number
of samples is needed to represent the posterior. A standard technique to increase the …