Design of Efficient Point-Mass Filter with Terrain Aided Navigation Illustration

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F23%3A43969674" target="_blank" >RIV/49777513:23520/23:43969674 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.23919/FUSION52260.2023.10224172" target="_blank" >https://doi.org/10.23919/FUSION52260.2023.10224172</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.23919/FUSION52260.2023.10224172" target="_blank" >10.23919/FUSION52260.2023.10224172</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Design of Efficient Point-Mass Filter with Terrain Aided Navigation Illustration

Popis výsledku v původním jazyce

This paper deals with state estimation of stochastic models with linear state dynamics, continuous or discrete in time. The emphasis is laid on a numerical solution to the state prediction by the time-update step of the grid-point-based point-mass filter (PMF), which is the most computationally demanding part of the PMF algorithm. A novel efficient PMF (ePMF) estimator, unifying continuous and discrete, approaches is proposed, designed, and discussed. By numerical illustrations, it is shown, that the proposed ePMF can lead to a time complexity reduction that exceeds 99.9% without compromising accuracy. The MATLAB® code of the ePMF is released with this paper.

Název v anglickém jazyce

Design of Efficient Point-Mass Filter with Terrain Aided Navigation Illustration

Popis výsledku anglicky

This paper deals with state estimation of stochastic models with linear state dynamics, continuous or discrete in time. The emphasis is laid on a numerical solution to the state prediction by the time-update step of the grid-point-based point-mass filter (PMF), which is the most computationally demanding part of the PMF algorithm. A novel efficient PMF (ePMF) estimator, unifying continuous and discrete, approaches is proposed, designed, and discussed. By numerical illustrations, it is shown, that the proposed ePMF can lead to a time complexity reduction that exceeds 99.9% without compromising accuracy. The MATLAB® code of the ePMF is released with this paper.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20205 - Automation and control systems

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 2023 26th International Conference on Information Fusion, FUSION 2023

ISBN

979-8-89034-485-4

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

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Název nakladatele

IEEE

Místo vydání

Charleston, SC, USA

Místo konání akce

Charleston, SC, USA

Datum konání akce

27. 6. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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