Forgetting factor Kalman filter with dependent noise processes

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26620%2F19%3APU134598" target="_blank" >RIV/00216305:26620/19:PU134598 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/CDC40024.2019.9029683" target="_blank" >http://dx.doi.org/10.1109/CDC40024.2019.9029683</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/CDC40024.2019.9029683" target="_blank" >10.1109/CDC40024.2019.9029683</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Forgetting factor Kalman filter with dependent noise processes

Popis výsledku v původním jazyce

The paper addresses the problem of filtering the state of a normal dynamical process with a dependency between the process and the measurement noise variables in the presence of an inaccurate model description. As regards the time occurrence of the noise dependency, we discuss the dependency structure where both the variables are correlated at the same time. An adaptive formulation of the Kalman filter (KF) is designed in order to mitigate the impact of the process model uncertainty on the degradation of the filter performance. The filter we propose exploits the collaborative decision to introduce a variable forgetting factor into the time update to reduce artificially the effect of obsolete knowledge on the filtering solution. Within the decision-making rules, a loss functional quantifying the time update is constructed to optimally combine the prediction alternatives possessing the form of the normal probability density function (pdf). The result is an adjustment of the Kalman gain matrix in response to empirically confirmed performance.

Název v anglickém jazyce

Forgetting factor Kalman filter with dependent noise processes

Popis výsledku anglicky

The paper addresses the problem of filtering the state of a normal dynamical process with a dependency between the process and the measurement noise variables in the presence of an inaccurate model description. As regards the time occurrence of the noise dependency, we discuss the dependency structure where both the variables are correlated at the same time. An adaptive formulation of the Kalman filter (KF) is designed in order to mitigate the impact of the process model uncertainty on the degradation of the filter performance. The filter we propose exploits the collaborative decision to introduce a variable forgetting factor into the time update to reduce artificially the effect of obsolete knowledge on the filtering solution. Within the decision-making rules, a loss functional quantifying the time update is constructed to optimally combine the prediction alternatives possessing the form of the normal probability density function (pdf). The result is an adjustment of the Kalman gain matrix in response to empirically confirmed performance.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20205 - Automation and control systems

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

2019

Kód důvěrnosti údajů

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

Název statě ve sborníku

58th Conference on Decision and Control

ISBN

978-1-7281-1397-5

ISSN

—

e-ISSN

—

Počet stran výsledku

7

Strana od-do

1809-1815

Název nakladatele

IEEE

Místo vydání

Nice, France

Místo konání akce

Nice, Francie

Datum konání akce

11. 12. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000560779001116