This paper considers the state estimation of linear discrete-time systems with uncertain-delayed observations. Using a Gaussian approximation, a sub-optimal, recursive, nonlinear estimator is derived, and by means of a simulation study its performance is compared with that of the best linear filter based on the same observation model
This paper focuses on the filtering problems of nonlinear discrete-time stochastic dynamic systems, ...
The state estimation problem with observations which may or may not contain a signal at any sample t...
This paper studies an optimal state estimation (Kalman filtering) problem under the assumption that ...
In this study, the authors consider the receding horizon filtering problem for discrete-time linear ...
Measurement delays and model parametric uncertainties are meaningful issues in actual systems. Addre...
A state-dependent model and nonlinear operator-based approach to estimation and filtering is introdu...
This paper proposes a comparative analysis of different state estimation techniques on linear and no...
AbstractIn this paper, one-stage prediction, filtering, and fixed-point smoothing problems are addre...
The optimal linear estimation problems are investigated in this paper for a class of discrete linear...
We consider a discrete-time linear system with correlated Gaussian plant and observation noises and ...
It is demonstrated that the accuracy of estimation of a random signal from interrupted observations ...
In this paper, we consider the problem of state estimation for nonlinear systems when the ou...
Abstract: This paper deals with the state estimation of linear time-invariant discrete systems with ...
The problem of estimating the state of discrete-time linear systems when uncertainties affect the sy...
This paper introduces a new filter for linear continuous-time stochastic systems with delayed measur...
This paper focuses on the filtering problems of nonlinear discrete-time stochastic dynamic systems, ...
The state estimation problem with observations which may or may not contain a signal at any sample t...
This paper studies an optimal state estimation (Kalman filtering) problem under the assumption that ...
In this study, the authors consider the receding horizon filtering problem for discrete-time linear ...
Measurement delays and model parametric uncertainties are meaningful issues in actual systems. Addre...
A state-dependent model and nonlinear operator-based approach to estimation and filtering is introdu...
This paper proposes a comparative analysis of different state estimation techniques on linear and no...
AbstractIn this paper, one-stage prediction, filtering, and fixed-point smoothing problems are addre...
The optimal linear estimation problems are investigated in this paper for a class of discrete linear...
We consider a discrete-time linear system with correlated Gaussian plant and observation noises and ...
It is demonstrated that the accuracy of estimation of a random signal from interrupted observations ...
In this paper, we consider the problem of state estimation for nonlinear systems when the ou...
Abstract: This paper deals with the state estimation of linear time-invariant discrete systems with ...
The problem of estimating the state of discrete-time linear systems when uncertainties affect the sy...
This paper introduces a new filter for linear continuous-time stochastic systems with delayed measur...
This paper focuses on the filtering problems of nonlinear discrete-time stochastic dynamic systems, ...
The state estimation problem with observations which may or may not contain a signal at any sample t...
This paper studies an optimal state estimation (Kalman filtering) problem under the assumption that ...
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