An Ef?cient Constrained Gaussian Particle Filter

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F11%3A43898216" target="_blank" >RIV/49777513:23520/11:43898216 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.3182/20110828-6-IT-1002.01833" target="_blank" >http://dx.doi.org/10.3182/20110828-6-IT-1002.01833</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3182/20110828-6-IT-1002.01833" target="_blank" >10.3182/20110828-6-IT-1002.01833</a>

Jazyk výsledku

angličtina

Název v původním jazyce

An Ef?cient Constrained Gaussian Particle Filter

Popis výsledku v původním jazyce

The paper deals with a state estimation of nonlinear stochastic dynamic systems subject to a nonlinear inequality constraint. A special focus is paid to particle filters, which provide an estimate of the whole probability density as opposed to the localfilters, such as the extended Kalman filter or the unscented Kalman filter, which provide a point estimate only. Within the particle filtering framework, there are several approaches to the constrained state estimation, mostly based on discarding samplesviolating the constraint with a possible increase of their number to improve the estimate quality. The paper aims at proposing a modi?cation to an importance function of the particle filter in order to increase ef?ciency of sampling while keeping the computational complexity low. The proposed modi?cation is utilized within the Gaussian particle filter which is advantageous for its low computational complexity. Complexity and estimation quality of the proposed constrained Gaussian partic

Název v anglickém jazyce

An Ef?cient Constrained Gaussian Particle Filter

Popis výsledku anglicky

The paper deals with a state estimation of nonlinear stochastic dynamic systems subject to a nonlinear inequality constraint. A special focus is paid to particle filters, which provide an estimate of the whole probability density as opposed to the localfilters, such as the extended Kalman filter or the unscented Kalman filter, which provide a point estimate only. Within the particle filtering framework, there are several approaches to the constrained state estimation, mostly based on discarding samplesviolating the constraint with a possible increase of their number to improve the estimate quality. The paper aims at proposing a modi?cation to an importance function of the particle filter in order to increase ef?ciency of sampling while keeping the computational complexity low. The proposed modi?cation is utilized within the Gaussian particle filter which is advantageous for its low computational complexity. Complexity and estimation quality of the proposed constrained Gaussian partic

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

BC - Teorie a systémy řízení

OECD FORD obor

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Projekt

<a href="/cs/project/GAP103%2F11%2F1353" target="_blank" >GAP103/11/1353: Odhad stavu dynamických stochastických systémů</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2011

Kód důvěrnosti údajů

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

Název periodika

IFAC Proceedings Volumes (IFAC-PapersOnline)

ISSN

1474-6670

e-ISSN

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Svazek periodika

18

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

IT - Italská republika

Počet stran výsledku

6

Strana od-do

11973-11978

Kód UT WoS článku

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EID výsledku v databázi Scopus

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