A Review on Kalman Filter Models

Kalman Filter (KF) that is also known as linear quadratic estimation filter estimates current states of a system through time as recursive using input measurements in mathematical process model. Thus algorithm is implemented in two steps: in the prediction step an estimation of current state of vari...

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Published in:	Archives of computational methods in engineering Vol. 30; no. 1; pp. 727 - 747
Main Authors:	Khodarahmi, Masoud, Maihami, Vafa
Format:	Journal Article
Language:	English
Published:	Dordrecht Springer Netherlands 01.01.2023 Springer Nature B.V
Subjects:	Algorithms Embedded systems Engineering Estimation Kalman filters Mathematical and Computational Engineering Nonlinear systems Review Article Signal processing Time series
ISSN:	1134-3060, 1886-1784
Online Access:	Get full text
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Abstract	Kalman Filter (KF) that is also known as linear quadratic estimation filter estimates current states of a system through time as recursive using input measurements in mathematical process model. Thus algorithm is implemented in two steps: in the prediction step an estimation of current state of variables in uncertainty conditions is presented. In the next step, after obtaining the measurement, previous estimation is updated by weighted arithmetic mean. Accordingly, using KF in non-linear systems can be difficult. For nonlinear systems Extended KF (EKF) and Unscented KF (UKF) represent the first-order and higher order linear approximations. KF cannot predict appropriate values for modeling system behavior in more complicated systems. In the current study, in addition to referring to basic methods, a review on recent researches on Multiple Model (MM) filters has been done. More reliable estimations obtain by using two or more filters with different models in parallel, by allocating an estimation to each filter, outputs of each filter are calculated. MM Adaptive Estimation (MMAE) and Interacting MM (IMM) are the most used methods for estimating MMs.
AbstractList	Kalman Filter (KF) that is also known as linear quadratic estimation filter estimates current states of a system through time as recursive using input measurements in mathematical process model. Thus algorithm is implemented in two steps: in the prediction step an estimation of current state of variables in uncertainty conditions is presented. In the next step, after obtaining the measurement, previous estimation is updated by weighted arithmetic mean. Accordingly, using KF in non-linear systems can be difficult. For nonlinear systems Extended KF (EKF) and Unscented KF (UKF) represent the first-order and higher order linear approximations. KF cannot predict appropriate values for modeling system behavior in more complicated systems. In the current study, in addition to referring to basic methods, a review on recent researches on Multiple Model (MM) filters has been done. More reliable estimations obtain by using two or more filters with different models in parallel, by allocating an estimation to each filter, outputs of each filter are calculated. MM Adaptive Estimation (MMAE) and Interacting MM (IMM) are the most used methods for estimating MMs.
Author	Maihami, Vafa Khodarahmi, Masoud
Author_xml	– sequence: 1 givenname: Masoud surname: Khodarahmi fullname: Khodarahmi, Masoud organization: Department of Computer Engineering, Islamic Azad University – sequence: 2 givenname: Vafa orcidid: 0000-0002-0478-6317 surname: Maihami fullname: Maihami, Vafa email: Maihami@iausdj.ac.ir organization: Department of Computer Engineering, Islamic Azad University
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Snippet	Kalman Filter (KF) that is also known as linear quadratic estimation filter estimates current states of a system through time as recursive using input...
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SubjectTerms	Algorithms Embedded systems Engineering Estimation Kalman filters Mathematical and Computational Engineering Nonlinear systems Review Article Signal processing Time series
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