A Software Framework and Tool for Nonlinear State Estimation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F09%3A00502481" target="_blank" >RIV/49777513:23520/09:00502481 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.3182/20090706-3-FR-2004.00084" target="_blank" >http://dx.doi.org/10.3182/20090706-3-FR-2004.00084</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3182/20090706-3-FR-2004.00084" target="_blank" >10.3182/20090706-3-FR-2004.00084</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A Software Framework and Tool for Nonlinear State Estimation

Popis výsledku v původním jazyce

The goal of the article is to describe a software framework designed for nonlinear state estimation of discrete time dynamic systems. The framework was designed with the aim to facilitate implementation, testing and use of various nonlinear state estimation methods in mind. The main strength of the framework is its versatility due to the possibility of either structural or probabilistic description of the problem. Besides the well-known basic nonlinear estimation methods such as the extended Kalman filter, the divided difference filters and the unscented Kalman filter, the framework implements particle filter with advanced features as well. As the framework is designed on the object oriented basis, further extension by user-specified nonlinear estimation algorithms is extremely easy. The paper provides a brief introduction into nonlinear state estimation problem and describes the individual components of the framework, their key features and use. The strengths of the framework are pres

Název v anglickém jazyce

A Software Framework and Tool for Nonlinear State Estimation

Popis výsledku anglicky

The goal of the article is to describe a software framework designed for nonlinear state estimation of discrete time dynamic systems. The framework was designed with the aim to facilitate implementation, testing and use of various nonlinear state estimation methods in mind. The main strength of the framework is its versatility due to the possibility of either structural or probabilistic description of the problem. Besides the well-known basic nonlinear estimation methods such as the extended Kalman filter, the divided difference filters and the unscented Kalman filter, the framework implements particle filter with advanced features as well. As the framework is designed on the object oriented basis, further extension by user-specified nonlinear estimation algorithms is extremely easy. The paper provides a brief introduction into nonlinear state estimation problem and describes the individual components of the framework, their key features and use. The strengths of the framework are pres

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

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2009

Kód důvěrnosti údajů

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

Název periodika

IFAC-PapersOnline

ISSN

1474-6670

e-ISSN

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Svazek periodika

15

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

FR - Francouzská republika

Počet stran výsledku

6

Strana od-do

510-515

Kód UT WoS článku

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EID výsledku v databázi Scopus

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