This paper considers the state-estimation problem with a constraint on the data-injection
gain. Special cases of this problem include the enforcing of a linear equality constraint in the …

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 …

This article is concerned with the state estimation problem for linear systems with linear state
equality constraints. We re-examine constrained Kalman filter variations and propose an …

This paper presents the optimal two-stage Kalman filter for systems that involve noise-free
observations and constant but unknown bias. Like the full-order separate-bias Kalman filter …

Simultaneous input and state estimation algorithms are studied as particular limits of Kalman
filtering problems. This admits interpretation of the algorithm properties and critical analysis …

We study the Kalman filtering problem when part or all of the observation measurements are
lost in a random fashion. We formulate the Kalman filtering problem with partial observation …

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 …

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 …

The authors provide the optimal solution of a two-stage estimation problem in the presence
of random bias. Under an algebraic constraint, the optimal estimate of the system state can …

The extended Kalman filter is an approximate filter for nonlinear systems, based on first-
order linearization. Its use for the joint parameter and state estimation problem for linear …