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 …

Real-time control systems rely on reliable estimates of states and parameters in order to
provide accurate and safe control of electro-mechanical systems. The task of extracting state …

Multitarget tracking (MTT) refers to the problem of jointly estimating the number of targets
and their states or trajectories from noisy sensor measurements. MTT has a long history …

In this paper, we present a new nonlinear filter for high-dimensional state estimation, which
we have named the cubature Kalman filter (CKF). The heart of the CKF is a spherical-radial …

An algorithm, the bootstrap filter, is proposed for implementing recursive Bayesian filters.
The required density of the state vector is represented as a set of random samples, which …

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 theoretically optimal approach to multisensor-multitarget detection, tracking, and
identification is a suitable generalization of the recursive Bayes nonlinear filter. Even in …

A new recursive algorithm is proposed for jointly estimating the time-varying number of
targets and their states from a sequence of observation sets in the presence of data …

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 …

Kalman Filtering Page 1 Kalman Filtering Charles K. Chui Guanrong Chen with Real-Time
Applications Fifth Edition Page 2 Kalman Filtering Page 3 Charles K. Chui • Guanrong Chen …