We review the problem of estimating parameters and unobserved trajectory components
from noisy time series measurements of continuous nonlinear dynamical systems. It is first …

A concise summary of techniques for multiple target tracking is provided and their main
characterics assessed qualitatively. The techniques have been catergorised into more than …

The definitive textbook and professional reference on Kalman Filtering–fully updated,
revised, and expanded This book contains the latest developments in the implementation …

Most newcomers to the field of linear stochastic estimation go through a difficult process in
understanding and applying the theory. This book minimizes the process while introducing …

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 …

In this paper, we extend the cubature Kalman filter (CKF) to deal with nonlinear state-space
models of the continuous-discrete kind. To be consistent with the literature, the resulting …

Nonlinear filters can provide estimation accuracy that is vastly superior to extended Kalman
filters for some important practical applications. We compare several types of nonlinear …

This paper considers the application of the unscented Kalman filter (UKF) to continuous-time
filtering problems, where both the state and measurement processes are modeled as …

This paper studies the problem of tracking a ballistic object in the reentry phase by
processing radar measurements. A suitable (highly nonlinear) model of target motion is …

In this paper we described the ensemble Kalman filter algorithm. This approach to nonlinear
Kalman filtering is a Monte Carlo procedure, which has been widely used in weather …