Performance and stochastic stability of the adaptive fading extended Kalman filter with the matrix forgetting factor
In this paper, the stability of the adaptive fading extended Kalman filter with the matrix forgetting factor when applied to the state estimation problem with noise terms in the non–linear discrete–time stochastic systems has been analysed. The analysis is conducted in a similar manner to the standa...
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De Gruyter
2016-01-01
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Series: | Open Mathematics |
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Online Access: | http://www.degruyter.com/view/j/math.2016.14.issue-1/math-2016-0083/math-2016-0083.xml?format=INT |
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author | Biçer Cenker Özbek Levent Erbay Hasan |
author_facet | Biçer Cenker Özbek Levent Erbay Hasan |
author_sort | Biçer Cenker |
collection | DOAJ |
description | In this paper, the stability of the adaptive fading extended Kalman filter with the matrix forgetting factor when applied to the state estimation problem with noise terms in the non–linear discrete–time stochastic systems has been analysed. The analysis is conducted in a similar manner to the standard extended Kalman filter’s stability analysis based on stochastic framework. The theoretical results show that under certain conditions on the initial estimation error and the noise terms, the estimation error remains bounded and the state estimation is stable. |
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institution | Directory Open Access Journal |
issn | 2391-5455 |
language | English |
last_indexed | 2024-12-21T14:21:53Z |
publishDate | 2016-01-01 |
publisher | De Gruyter |
record_format | Article |
series | Open Mathematics |
spelling | doaj.art-411f17fd7ff248c5adae6a27772cd95e2022-12-21T19:00:45ZengDe GruyterOpen Mathematics2391-54552016-01-0114193494510.1515/math-2016-0083math-2016-0083Performance and stochastic stability of the adaptive fading extended Kalman filter with the matrix forgetting factorBiçer Cenker0Özbek Levent1Erbay Hasan2Statistics Department, Arts & Science Faculty, Kırıkkale University, 71450 Kırıkkale, TurkeyDepartment of Statistics, Faculty of Science, Ankara University, 06100, Tandoğan, Ankara, TurkeyComputer Engineering Department, Engineering Faculty, Kırıkkale University, Yahşhan, 71450 Kırıkkale, TurkeyIn this paper, the stability of the adaptive fading extended Kalman filter with the matrix forgetting factor when applied to the state estimation problem with noise terms in the non–linear discrete–time stochastic systems has been analysed. The analysis is conducted in a similar manner to the standard extended Kalman filter’s stability analysis based on stochastic framework. The theoretical results show that under certain conditions on the initial estimation error and the noise terms, the estimation error remains bounded and the state estimation is stable.http://www.degruyter.com/view/j/math.2016.14.issue-1/math-2016-0083/math-2016-0083.xml?format=INTkalman filternon-linear systemsstabilityadaptive filtering15a5137hxx |
spellingShingle | Biçer Cenker Özbek Levent Erbay Hasan Performance and stochastic stability of the adaptive fading extended Kalman filter with the matrix forgetting factor Open Mathematics kalman filter non-linear systems stability adaptive filtering 15a51 37hxx |
title | Performance and stochastic stability of the adaptive fading extended Kalman filter with the matrix forgetting factor |
title_full | Performance and stochastic stability of the adaptive fading extended Kalman filter with the matrix forgetting factor |
title_fullStr | Performance and stochastic stability of the adaptive fading extended Kalman filter with the matrix forgetting factor |
title_full_unstemmed | Performance and stochastic stability of the adaptive fading extended Kalman filter with the matrix forgetting factor |
title_short | Performance and stochastic stability of the adaptive fading extended Kalman filter with the matrix forgetting factor |
title_sort | performance and stochastic stability of the adaptive fading extended kalman filter with the matrix forgetting factor |
topic | kalman filter non-linear systems stability adaptive filtering 15a51 37hxx |
url | http://www.degruyter.com/view/j/math.2016.14.issue-1/math-2016-0083/math-2016-0083.xml?format=INT |
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