Truncation nonlinear filters for state estimation with nonlinear inequality constraints

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F12%3A43897452" target="_blank" >RIV/49777513:23520/12:43897452 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.automatica.2011.11.002" target="_blank" >http://dx.doi.org/10.1016/j.automatica.2011.11.002</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.automatica.2011.11.002" target="_blank" >10.1016/j.automatica.2011.11.002</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Truncation nonlinear filters for state estimation with nonlinear inequality constraints

Popis výsledku v původním jazyce

The paper focuses on the state estimation problem of nonlinear non-Gaussian systems with state subject to a nonlinear inequality constraint. Taking into account the available additional information about the state given by the constraint increases the estimate quality compared to classical state estimation methods which cannot utilize the information. Considering the constraint in the form of an inequality involving a nonlinear function of the state makes the state estimation problem difficult and hencetreated only marginally. In this paper, a generic local filter for the inequality constrained estimation problem is proposed. It covers the extended Kalman filter, unscented Kalman filter, and divided difference filter as special cases and enforces theconstraint by truncating the conditional density of the state. The same idea is then utilized in a truncation Gaussian mixture filter which is also proposed in the paper to increase the estimate quality further by providing a global const

Název v anglickém jazyce

Truncation nonlinear filters for state estimation with nonlinear inequality constraints

Popis výsledku anglicky

The paper focuses on the state estimation problem of nonlinear non-Gaussian systems with state subject to a nonlinear inequality constraint. Taking into account the available additional information about the state given by the constraint increases the estimate quality compared to classical state estimation methods which cannot utilize the information. Considering the constraint in the form of an inequality involving a nonlinear function of the state makes the state estimation problem difficult and hencetreated only marginally. In this paper, a generic local filter for the inequality constrained estimation problem is proposed. It covers the extended Kalman filter, unscented Kalman filter, and divided difference filter as special cases and enforces theconstraint by truncating the conditional density of the state. The same idea is then utilized in a truncation Gaussian mixture filter which is also proposed in the paper to increase the estimate quality further by providing a global const

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

2012

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

48

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

14

Strana od-do

"273?286"

Kód UT WoS článku

000301213200004

EID výsledku v databázi Scopus

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