Home > Research > Publications & Outputs > State estimation by IMM filter in the presence ...

Research

Electronic data

Keywords

View graph of relations

State estimation by IMM filter in the presence of structural uncertainty

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter

Published

Standard

State estimation by IMM filter in the presence of structural uncertainty. / Mihaylova, L.
Recent Advances in Signal Processing and Communications. ed. / N. Mastorakis. Greece: World Scientific and Engineering Society (WSES) Press, 1999. p. 83-88.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter

Harvard

Mihaylova, L 1999, State estimation by IMM filter in the presence of structural uncertainty. in N Mastorakis (ed.), Recent Advances in Signal Processing and Communications. World Scientific and Engineering Society (WSES) Press, Greece, pp. 83-88.

APA

Mihaylova, L. (1999). State estimation by IMM filter in the presence of structural uncertainty. In N. Mastorakis (Ed.), Recent Advances in Signal Processing and Communications (pp. 83-88). World Scientific and Engineering Society (WSES) Press,.

Vancouver

Mihaylova L. State estimation by IMM filter in the presence of structural uncertainty. In Mastorakis N, editor, Recent Advances in Signal Processing and Communications. Greece: World Scientific and Engineering Society (WSES) Press,. 1999. p. 83-88

Author

Mihaylova, L. / State estimation by IMM filter in the presence of structural uncertainty. Recent Advances in Signal Processing and Communications. editor / N. Mastorakis. Greece : World Scientific and Engineering Society (WSES) Press, 1999. pp. 83-88

Bibtex

@inbook{2c9e1dd3edb444f5a7f65cd952cdd865,
title = "State estimation by IMM filter in the presence of structural uncertainty",
abstract = "A solution to the state estimation problem under structural uncertainty (unknown or changeable dimension of the system state space) is given by the Interacting Multiple Model (IMM) filter. The requirements for its applicability under structural uncertainty are formulated. The highest IMM model probability is an indicator for the true model order and it can be used for structural identification. Results from test examples with stationary systems and systems with structural nonstationarity (changeable structure in the course of the time) demonstrate the filter efficiency. The scalar and multivariable cases are investigated.",
keywords = "structural uncertainty, multiple model approach, state estimation, identification DCS-publications-id, incoll-70, DCS-publications-personnel-id, 121",
author = "L. Mihaylova",
year = "1999",
language = "English",
pages = "83--88",
editor = "N. Mastorakis",
booktitle = "Recent Advances in Signal Processing and Communications",
publisher = "World Scientific and Engineering Society (WSES) Press,",

}

RIS

TY - CHAP

T1 - State estimation by IMM filter in the presence of structural uncertainty

AU - Mihaylova, L.

PY - 1999

Y1 - 1999

N2 - A solution to the state estimation problem under structural uncertainty (unknown or changeable dimension of the system state space) is given by the Interacting Multiple Model (IMM) filter. The requirements for its applicability under structural uncertainty are formulated. The highest IMM model probability is an indicator for the true model order and it can be used for structural identification. Results from test examples with stationary systems and systems with structural nonstationarity (changeable structure in the course of the time) demonstrate the filter efficiency. The scalar and multivariable cases are investigated.

AB - A solution to the state estimation problem under structural uncertainty (unknown or changeable dimension of the system state space) is given by the Interacting Multiple Model (IMM) filter. The requirements for its applicability under structural uncertainty are formulated. The highest IMM model probability is an indicator for the true model order and it can be used for structural identification. Results from test examples with stationary systems and systems with structural nonstationarity (changeable structure in the course of the time) demonstrate the filter efficiency. The scalar and multivariable cases are investigated.

KW - structural uncertainty

KW - multiple model approach

KW - state estimation

KW - identification DCS-publications-id

KW - incoll-70

KW - DCS-publications-personnel-id

KW - 121

M3 - Chapter

SP - 83

EP - 88

BT - Recent Advances in Signal Processing and Communications

A2 - Mastorakis, N.

PB - World Scientific and Engineering Society (WSES) Press,

CY - Greece

ER -