Marginalized adaptive particle filtering for nonlinear models with unknown time-varying noise parameters

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F13%3A00393047" target="_blank" >RIV/67985556:_____/13:00393047 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.automatica.2013.02.046" target="_blank" >http://dx.doi.org/10.1016/j.automatica.2013.02.046</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.automatica.2013.02.046" target="_blank" >10.1016/j.automatica.2013.02.046</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Marginalized adaptive particle filtering for nonlinear models with unknown time-varying noise parameters

Popis výsledku v původním jazyce

Knowledge of noise distribution is typically crucial for good estimation of a non-linear state-space model. However, properties of the noise process are often unknown in the majority of practical applications. Moreover, distribution of the noise may be non-stationary or state dependent, which prevents the use of off-line tuning methods. General estimation methods, such as particle filtering can be used to estimate the noise parameters, however at the price of heavy computational load. In this paper, wepresent an approach based on marginalized particle filtering where the noise parameters have analytical distribution. Explicit modeling of parameter non-stationarity is avoided and it is replaced by maximum-entropy estimation based on the assumption of slowly varying parameters. Properties of the resulting algorithm are illustrated on both a standard example and a navigation application based on odometry. The latter involves formulas for dead reckoning rotational speeds of two wheels wit

Název v anglickém jazyce

Marginalized adaptive particle filtering for nonlinear models with unknown time-varying noise parameters

Popis výsledku anglicky

Knowledge of noise distribution is typically crucial for good estimation of a non-linear state-space model. However, properties of the noise process are often unknown in the majority of practical applications. Moreover, distribution of the noise may be non-stationary or state dependent, which prevents the use of off-line tuning methods. General estimation methods, such as particle filtering can be used to estimate the noise parameters, however at the price of heavy computational load. In this paper, wepresent an approach based on marginalized particle filtering where the noise parameters have analytical distribution. Explicit modeling of parameter non-stationarity is avoided and it is replaced by maximum-entropy estimation based on the assumption of slowly varying parameters. Properties of the resulting algorithm are illustrated on both a standard example and a navigation application based on odometry. The latter involves formulas for dead reckoning rotational speeds of two wheels wit

Druh

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

CEP obor

BC - Teorie a systémy řízení

OECD FORD obor

—

Projekt

<a href="/cs/project/GAP102%2F11%2F0437" target="_blank" >GAP102/11/0437: Regulace a identifikace parametrů střídavých elektrických pohonů v kritických provozních stavech</a><br>

Návaznosti

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

Rok uplatnění

2013

Kód důvěrnosti údajů

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

Název periodika

Automatica

ISSN

0005-1098

e-ISSN

—

Svazek periodika

49

Číslo periodika v rámci svazku

6

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

10

Strana od-do

1566-1575

Kód UT WoS článku

000319540500005

EID výsledku v databázi Scopus

—