<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This paper deals with the problem of joint state and unknown input estimation for stochastic discrete-time linear systems subject to intermittent unknown inputs on measurements. A Kalman filter approach is proposed for state prediction and intermittent unknown input reconstruction. The filter design is based on the minimization of the trace of the state estimation error covariance matrix under the constraint that the state prediction error is decoupled from active unknown inputs corrupting measurements at the current time. When the system is not strongly detectable, a sufficient stochastic stability condition on the mathematical expectation of the random state prediction errors covariance matrix is established in the case where the arrival binary sequences of unknown inputs follow independent random Bernoulli processes. When the intermittent unknown inputs on measurements represent intermittent observations, an illustrative example shows that the proposed filter corresponds to a Kalman filter with intermittent observations having the ability to generate a minimum variance unbiased prediction of measurement losses.</p>
</abstract>

This paper deals with the problem of joint state and unknown input estimation for stochastic discrete-time linear systems subject to intermittent unknown inputs on measurements. A Kalman filter approach is proposed for state prediction and intermittent unknown input reconstruction. The filter design is based on the minimization of the trace of the state estimation error covariance matrix under the constraint that the state prediction error is decoupled from active unknown inputs corrupting measurements at the current time. When the system is not strongly detectable, a sufficient stochastic stability condition on the mathematical expectation of the random state prediction errors covariance matrix is established in the case where the arrival binary sequences of unknown inputs follow independent random Bernoulli processes. When the intermittent unknown inputs on measurements represent intermittent observations, an illustrative example shows that the proposed filter corresponds to a Kalman filter with intermittent observations having the ability to generate a minimum variance unbiased prediction of measurement losses.

A Kalman Filter with Intermittent Observations and Reconstruction of Data Losses

Laboratory of Modeling, Analysis and Control, National Engineering School of Gabes

Henri Poincaré University Institute of Technology of Longwy, University of Lorraine

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"A Kalman Filter with Intermittent Observations and Reconstruction of Data Losses"}

This paper deals with the problem of joint state and unknown input estimation for stochastic discrete-time linear systems subject to intermittent unknown...

A Kalman Filter with Intermittent Observations and Reconstruction of Data Losses

Published Online: Jul 04, 2022

Page range: 241 - 253

Received: Dec 29, 2021

Accepted: Apr 09, 2022

DOI: https://doi.org/10.34768/amcs-2022-0018

Keywords
Kalman filter, intermittent unknown inputs, linear system, intermittent observation

© 2022 Taouba Rhouma et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

A Kalman Filter with Intermittent Observations and Reconstruction of Data Losses

Published Online: Jul 04, 2022

Page range: 241 - 253

Received: Dec 29, 2021

Accepted: Apr 09, 2022

DOI: https://doi.org/10.34768/amcs-2022-0018

KeywordsKalman filter, intermittent unknown inputs, linear system, intermittent observation

© 2022 Taouba Rhouma et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
Kalman filter, intermittent unknown inputs, linear system, intermittent observation