Point-mass Filter with Non-equidistant Grid Design
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F25%3A43977735" target="_blank" >RIV/49777513:23520/25:43977735 - isvavai.cz</a>
Výsledek na webu
<a href="https://svk.fav.zcu.cz/download/proceedings_svk_2025.pdf" target="_blank" >https://svk.fav.zcu.cz/download/proceedings_svk_2025.pdf</a>
DOI - Digital Object Identifier
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Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Point-mass Filter with Non-equidistant Grid Design
Popis výsledku v původním jazyce
State estimation is essential in engineering applications from navigation to control. While many estimation methods assume Gaussian probability density functions (PDFs), global filters can capture more complex, non-Gaussian PDFs. The point-mass filter (PMF) is a global filter that discretises the state-space into a grid and approximates the PDF as a piecewise constant function, known as the point-mass density (PMD). Unlike particle filters, the PMF produces deterministic estimates: given the same measurements, it will always generate identical outputs.This deterministic nature, combined with its structured representation of the entire distribution,higher robustness and better handling of abrupt changes, makes it valuable in high-reliabilityapplications like navigation systems.
Název v anglickém jazyce
Point-mass Filter with Non-equidistant Grid Design
Popis výsledku anglicky
State estimation is essential in engineering applications from navigation to control. While many estimation methods assume Gaussian probability density functions (PDFs), global filters can capture more complex, non-Gaussian PDFs. The point-mass filter (PMF) is a global filter that discretises the state-space into a grid and approximates the PDF as a piecewise constant function, known as the point-mass density (PMD). Unlike particle filters, the PMF produces deterministic estimates: given the same measurements, it will always generate identical outputs.This deterministic nature, combined with its structured representation of the entire distribution,higher robustness and better handling of abrupt changes, makes it valuable in high-reliabilityapplications like navigation systems.
Klasifikace
Druh
O - Ostatní výsledky
CEP obor
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OECD FORD obor
20205 - Automation and control systems
Návaznosti výsledku
Projekt
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Návaznosti
S - Specificky vyzkum na vysokych skolach
Ostatní
Rok uplatnění
2025
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů