Stochastic Integration Filter: Theoretical and Implementation Aspects

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F18%3A43953135" target="_blank" >RIV/49777513:23520/18:43953135 - isvavai.cz</a>

Výsledek na webu

<a href="http://https:dx.doi.org/10.23919/ICIF.2018.8455586" target="_blank" >http://https:dx.doi.org/10.23919/ICIF.2018.8455586</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.23919/ICIF.2018.8455586" target="_blank" >10.23919/ICIF.2018.8455586</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Stochastic Integration Filter: Theoretical and Implementation Aspects

Popis výsledku v původním jazyce

The paper focuses on state estimation of discrete-time nonlinear stochastic dynamic systems with a special focus on the stochastic integration filter. The filter is an representative of the Gaussian filter and computes the state and measurement predictive moments by making use of a stochastic integration rule. As a result, the calculated values of the moments are random variables and exhibit favorable asymptotic properties. The paper analyzes theoretical consequences of using stochastic integration rules and proposes several modifications that improve the performance of the stochastic integration filter. As the filter requires multiple iterations of the stochastic rule, its computational costs are higher in comparison with other Gaussian filters. To reduce the costs, several modifications are proposed in the paper, which are also concerned with numerical stability issues. The proposed modifications are illustrated using both static and dynamic numerical examples used in target tracking.

Název v anglickém jazyce

Stochastic Integration Filter: Theoretical and Implementation Aspects

Popis výsledku anglicky

The paper focuses on state estimation of discrete-time nonlinear stochastic dynamic systems with a special focus on the stochastic integration filter. The filter is an representative of the Gaussian filter and computes the state and measurement predictive moments by making use of a stochastic integration rule. As a result, the calculated values of the moments are random variables and exhibit favorable asymptotic properties. The paper analyzes theoretical consequences of using stochastic integration rules and proposes several modifications that improve the performance of the stochastic integration filter. As the filter requires multiple iterations of the stochastic rule, its computational costs are higher in comparison with other Gaussian filters. To reduce the costs, several modifications are proposed in the paper, which are also concerned with numerical stability issues. The proposed modifications are illustrated using both static and dynamic numerical examples used in target tracking.

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

2018

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 21st International Conference on Information Fusion (FUSION 2018)

ISBN

978-0-9964527-6-2

ISSN

—

e-ISSN

neuvedeno

Počet stran výsledku

8

Strana od-do

1699-1706

Název nakladatele

IEEE

Místo vydání

Cambridge, UK

Místo konání akce

Cambridge, UK

Datum konání akce

10. 7. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—