This paper addresses globally optimal unbiased minimum-variance state estimation for
systems with unknown inputs that affect both the system and the output with the descriptor …

In this paper, we consider the global unbiased minimum-variance state estimation for
systems with unknown inputs which affect both the system and the output via the descriptor …

A parameterized three-stage Kalman filter (PTSKF) is proposed, serving as a unified solution
to unbiased minimum-variance estimation for systems with unknown inputs that affect both …

This paper is concerned with the optimal solution of two‐stage Kalman filtering for linear
discrete‐time stochastic time‐varying systems with unknown inputs affecting both the system …

The problem of joint input and state estimation is discussed in this paper for linear discrete-
time stochastic systems. By minimizing an objective function of weighted least squares …

This paper investigates the infinity augmented state Kalman filter (IASKF) to obtain the
unknown input and state estimation for systems with the rank-deficient matrix. With the aid of …

This paper considers the optimal unbiased minimumvariance estimation for systems with
unknown inputs that affect both the system model and the measurements. By making use of …

This study investigates the shortcomings of existing unscented type Kalman filters (UTKFs)
used for state estimation problem of non‐linear stochastic dynamic systems. There are three …

For nonlinear discrete systems with dual unknown inputs, there are many limitations
regarding previous nonlinear filters. This paper proposes two new, improved square-root …

This article proposes a systematic design methodology of unknown input estimation for time‐
varying systems in which unknown inputs exist in system dynamic and measurement …