Add to ORKGInternational audienceThis paper presents a comparison in terms of accuracy and complexity between two approaches used for state estimation of linear systems: a classic Kalman filter and a guaranteed set-membership state estimation technique. The main goal of this paper is to analyze the advantages of these techniques and to combine them in the future in a new accurate and simple extension that handles system uncertainties and chance constraints. Two academic examples illustrate the main differences between the compared techniques
For linear dynamic systems with white process and measurement noise, the Kalman filter is known to b...
In this report are discussed different algorithms for the state estimation problem according to the ...
This paper proposes a comparative analysis of different state estimation techniques on linear and no...
International audienceThis paper presents a comparison in terms of accuracy and complexity between t...
20th World Congress of the International Federation of Automatic Control, Jul 2017, Toulouse, France...
International audienceIn an uncertain framework the performance of two methods of state estimation f...
This paper deals with state estimation problem for linear systems with state equality constraints. U...
This paper presents the analysis and comparison of interval-observer-based and set-membership approa...
This article is concerned with the state estimation problem for linear systems with linear state equ...
Kalman filter is one of the best filter utilized as a part of the state estimation taking into accou...
This paper presents a setmembership state estimation scheme for linear systems with unknown but boun...
Applying the Kalman filtering scheme to linearized system dynamics and observation models does in ge...
State estimation techniques for centralized, distributed, and decentralized systems are studied. An ...
Abstract — State estimation theory is one of the best mathematical approaches to analyze variants in...
The Kalman filter is the minimum-variance state estimator for linear dynamic systems with Gaussian n...
For linear dynamic systems with white process and measurement noise, the Kalman filter is known to b...
In this report are discussed different algorithms for the state estimation problem according to the ...
This paper proposes a comparative analysis of different state estimation techniques on linear and no...
International audienceThis paper presents a comparison in terms of accuracy and complexity between t...
20th World Congress of the International Federation of Automatic Control, Jul 2017, Toulouse, France...
International audienceIn an uncertain framework the performance of two methods of state estimation f...
This paper deals with state estimation problem for linear systems with state equality constraints. U...
This paper presents the analysis and comparison of interval-observer-based and set-membership approa...
This article is concerned with the state estimation problem for linear systems with linear state equ...
Kalman filter is one of the best filter utilized as a part of the state estimation taking into accou...
This paper presents a setmembership state estimation scheme for linear systems with unknown but boun...
Applying the Kalman filtering scheme to linearized system dynamics and observation models does in ge...
State estimation techniques for centralized, distributed, and decentralized systems are studied. An ...
Abstract — State estimation theory is one of the best mathematical approaches to analyze variants in...
The Kalman filter is the minimum-variance state estimator for linear dynamic systems with Gaussian n...
For linear dynamic systems with white process and measurement noise, the Kalman filter is known to b...
In this report are discussed different algorithms for the state estimation problem according to the ...
This paper proposes a comparative analysis of different state estimation techniques on linear and no...