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Non-minimal state space polynomial form of the Kalman filter for a general noise model

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Non-minimal state space polynomial form of the Kalman filter for a general noise model. / Wilson, Emma Denise; Clairon, Q.; Taylor, Charles James.

In: Electronics Letters, Vol. 54, No. 4, 27.02.2018, p. 204-206.

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@article{2f317a89a69a4f2e8385d7d6a8ce1a1b,
title = "Non-minimal state space polynomial form of the Kalman filter for a general noise model",
abstract = "The optimal refined instrumental variable (RIV) method for the estimation of the Box-Jenkins (BJ) model is modified so that it functions as an optimal filter and state-estimation algorithm. In contrast to the previously developed minimal and non-minimal state space (NMSS) forms for an Auto-Regressive Moving Average with eXogenous variables (ARMAX) model, the new algorithm requires the introduction of a novel extended NMSS form. This facilitates representation of the more general noise component of the BJ model. The approach can be used for adaptive filtering and state variable feedback control.",
keywords = "state space methods, state estimation, state feedback, Kalman filters, linear quadratic control, control",
author = "Wilson, {Emma Denise} and Q. Clairon and Taylor, {Charles James}",
note = "{\textcopyright}2018 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.",
year = "2018",
month = feb
day = "27",
doi = "10.1049/el.2017.3577",
language = "English",
volume = "54",
pages = "204--206",
journal = "Electronics Letters",
issn = "0013-5194",
publisher = "Institution of Engineering and Technology",
number = "4",

}

RIS

TY - JOUR

T1 - Non-minimal state space polynomial form of the Kalman filter for a general noise model

AU - Wilson, Emma Denise

AU - Clairon, Q.

AU - Taylor, Charles James

N1 - ©2018 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PY - 2018/2/27

Y1 - 2018/2/27

N2 - The optimal refined instrumental variable (RIV) method for the estimation of the Box-Jenkins (BJ) model is modified so that it functions as an optimal filter and state-estimation algorithm. In contrast to the previously developed minimal and non-minimal state space (NMSS) forms for an Auto-Regressive Moving Average with eXogenous variables (ARMAX) model, the new algorithm requires the introduction of a novel extended NMSS form. This facilitates representation of the more general noise component of the BJ model. The approach can be used for adaptive filtering and state variable feedback control.

AB - The optimal refined instrumental variable (RIV) method for the estimation of the Box-Jenkins (BJ) model is modified so that it functions as an optimal filter and state-estimation algorithm. In contrast to the previously developed minimal and non-minimal state space (NMSS) forms for an Auto-Regressive Moving Average with eXogenous variables (ARMAX) model, the new algorithm requires the introduction of a novel extended NMSS form. This facilitates representation of the more general noise component of the BJ model. The approach can be used for adaptive filtering and state variable feedback control.

KW - state space methods

KW - state estimation

KW - state feedback

KW - Kalman filters

KW - linear quadratic control

KW - control

U2 - 10.1049/el.2017.3577

DO - 10.1049/el.2017.3577

M3 - Letter

VL - 54

SP - 204

EP - 206

JO - Electronics Letters

JF - Electronics Letters

SN - 0013-5194

IS - 4

ER -