Kalman filters are commonly used to estimate the states of a dynamic system. However, in
the application of Kalman filters there is often known model or signal information that is …

An analytic method was developed by D. Simon and TL Chia to incorporate linear state
equality constraints into the Kalman filter. When the state constraint was nonlinear …

The Kalman filter is the Bayesian optimum solution to the problem of sequentially estimating
the states of a dynamical system in which the state evolution and measurement processes …

The Kalman filter is the minimum-variance state estimator for linear dynamic systems with
Gaussian noise. Even if the noise is non-Gaussian, the Kalman filter is the best linear …

A simple procedure to include state inequality constraints in the unscented Kalman filter is
proposed. With this procedure, the information of active state constraints influences the state …

In higher order Kalman filtering applications the analyst often has very little insight into the
nature of the observability of the system. For example, there are situations where the filter …

This report presents and derives the Kalman filter and the Extended Kalman filter dynamics.
The general filtering problem is formulated and it is shown that, under linearity and Gaussian …

The Kalman filter, rooted in the state‐space formulation of linear dynamical systems,
provides a recursive solution to the linear optimal filtering problem. It applies to stationary as …

THE Kalman filter is widely used today for state estimation in aerospace systems. This well-
known filter made the transition from a relatively abstract theory to application in many …

This paper compares state estimation techniques for nonlinear stochastic dynamic systems,
which are important for target tracking. Recently, several methods for nonlinear state …