Diffusion in Lagrangian Grid-based Predictors

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F25%3A43976501" target="_blank" >RIV/49777513:23520/25:43976501 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.23919/FUSION65864.2025.11124123" target="_blank" >https://doi.org/10.23919/FUSION65864.2025.11124123</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.23919/FUSION65864.2025.11124123" target="_blank" >10.23919/FUSION65864.2025.11124123</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Diffusion in Lagrangian Grid-based Predictors

Popis výsledku v původním jazyce

This paper focuses on state prediction for stochastic dynamic models with linear dynamics, emphasizing a recently proposed efficient and robust Lagrangian approach for solving the Chapman-Kolmogorov equation. In contrast to the standard Eulerian perspective, the Lagrangian method separates the solution into two sequential steps: advection and diffusion. Advection is handled by moving a carefully designed grid, while diffusion is addressed using the convolution theorem. This approach significantly reduces computational complexity while preserving the same accuracy. In this paper, we propose formulating diffusion as a continuous-time process, leading to a partial differential equation (PDE). Various methods for solving this PDE are presented and compared within a unified framework, along with evaluations of their properties and example implementations. We demonstrate that the continuous formulation can yield substantial reductions in computational complexity with only marginal loss in accuracy.

Název v anglickém jazyce

Diffusion in Lagrangian Grid-based Predictors

Popis výsledku anglicky

This paper focuses on state prediction for stochastic dynamic models with linear dynamics, emphasizing a recently proposed efficient and robust Lagrangian approach for solving the Chapman-Kolmogorov equation. In contrast to the standard Eulerian perspective, the Lagrangian method separates the solution into two sequential steps: advection and diffusion. Advection is handled by moving a carefully designed grid, while diffusion is addressed using the convolution theorem. This approach significantly reduces computational complexity while preserving the same accuracy. In this paper, we propose formulating diffusion as a continuous-time process, leading to a partial differential equation (PDE). Various methods for solving this PDE are presented and compared within a unified framework, along with evaluations of their properties and example implementations. We demonstrate that the continuous formulation can yield substantial reductions in computational complexity with only marginal loss in accuracy.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20205 - Automation and control systems

Projekt

<a href="/cs/project/GC25-16919J" target="_blank" >GC25-16919J: Pokročilé metody odhadu stavu v úloze sledování mnoha cílů</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název statě ve sborníku

2025 28th International Conference on Information Fusion (FUSION)

ISBN

978-1-03-705623-9

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

1-8

Název nakladatele

IEEE

Místo vydání

Rio de Janiero, Brazílie

Místo konání akce

Rio de Janiero, Brazílie

Datum konání akce

7. 7. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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