Structure Adaptation of Nonlinear Filters based on Non-Gaussianity Measures

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F15%3A43925644" target="_blank" >RIV/49777513:23520/15:43925644 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/ACC.2015.7171819" target="_blank" >http://dx.doi.org/10.1109/ACC.2015.7171819</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ACC.2015.7171819" target="_blank" >10.1109/ACC.2015.7171819</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Structure Adaptation of Nonlinear Filters based on Non-Gaussianity Measures

Popis výsledku v původním jazyce

The paper deals with state estimation of stochastic nonlinear dynamical systems. A structure adaptation of nonlinear filters is proposed to reduce errors stemming from approximations made by the filters. The adaptation is controlled by non-Gaussian measures which assess current working conditions of the filter. A large non-Gaussian measure indicates a possible large approximation error and results in splitting the state conditional probability density function. To limit computational complexity of the filter given by the number of terms, a reduction of this number is done by merging some terms. The algorithm of the proposed filter with structure adaptation is detailed using the extended Kalman filter relations. Performance of the filter is illustrated in a numerical example.

Název v anglickém jazyce

Structure Adaptation of Nonlinear Filters based on Non-Gaussianity Measures

Popis výsledku anglicky

The paper deals with state estimation of stochastic nonlinear dynamical systems. A structure adaptation of nonlinear filters is proposed to reduce errors stemming from approximations made by the filters. The adaptation is controlled by non-Gaussian measures which assess current working conditions of the filter. A large non-Gaussian measure indicates a possible large approximation error and results in splitting the state conditional probability density function. To limit computational complexity of the filter given by the number of terms, a reduction of this number is done by merging some terms. The algorithm of the proposed filter with structure adaptation is detailed using the extended Kalman filter relations. Performance of the filter is illustrated in a numerical example.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20205 - Automation and control systems

Projekt

<a href="/cs/project/GA15-12068S" target="_blank" >GA15-12068S: Adaptivní přístupy k odhadu stavu nelineárních stochastických systémů</a><br>

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 2015 American Control Conference

ISBN

978-1-4799-8684-2

ISSN

0743-1619

e-ISSN

—

Počet stran výsledku

6

Strana od-do

3162-3167

Název nakladatele

IEEE

Místo vydání

Chicago

Místo konání akce

Chicago, USA

Datum konání akce

1. 7. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000370259203042