Autor:

Rhouma, Taouba ; Keller, Jean Yves ; Abdelkrim, Mohamed Naceur

Współtwórca:

Witczak, Marcin - ed. ; Stetter, Ralf - ed.

Tytuł:

A Kalman filter with intermittent observations and reconstruction of data losses

Podtytuł:

.

Tytuł publikacji grupowej:

AMCS, volume 32 (2022)

Temat i słowa kluczowe:

Kalman filters ; intermittent unknown inputs ; linear systems ; intermittent observation

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.

Wydawca:

Zielona Góra: Uniwersytet Zielonogórski

Data wydania:

2022

Typ zasobu:

artykuł

DOI:

10.34768/amcs-2022-0018

Strony:

241-253

Źródło:

AMCS, volume 32, number 2 (2022) ; kliknij tutaj, żeby przejść

Jezyk:

eng

Licencja CC BY 4.0:

kliknij tutaj, żeby przejść

Prawa do dysponowania publikacją:

Biblioteka Uniwersytetu Zielonogórskiego