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 …

Since the landmark work of RE Kalman in the 1960s, considerable efforts have been
devoted to time series state space models for a large variety of dynamic estimation …

A dual implementation of the Kalman filter is proposed for estimating the unknown input and
states of a linear state-space model by using sparse noisy acceleration measurements. The …

This article presents an up-to-date tutorial review of nonlinear Bayesian estimation. State
estimation for nonlinear systems has been a challenge encountered in a wide range of …

This paper addresses the problem of simultaneously estimating the state and the input of a
linear discrete-time system. A recursive filter, optimal in the minimum-variance unbiased …

This paper extends previous work on joint input and state estimation to systems with direct
feedthrough of the unknown input to the output. Using linear minimum-variance unbiased …

An algorithm is presented for jointly estimating the input and state of a structure from a
limited number of acceleration measurements. The algorithm extends an existing joint input …

Multi-autonomous underwater vehicles (AUVs) cooperative localization has become a
research hotspot in the marine navigation field. In this paper, a filtering algorithm for slave …

The classical recursive three‐step filter can be used to estimate the state and unknown input
when the system is affected by unknown input, but the recursive three‐step filter cannot be …

In this paper, we present a unified optimal and exponentially stable filter for linear discrete-
time stochastic systems that simultaneously estimates the states and unknown inputs in an …