Lagrangian Grid-Based Filters With Application to Terrain-Aided Navigation
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F25%3A43976497" target="_blank" >RIV/49777513:23520/25:43976497 - isvavai.cz</a>
Výsledek na webu
<a href="https://doi.org/10.1109/MSP.2024.3489969" target="_blank" >https://doi.org/10.1109/MSP.2024.3489969</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/MSP.2024.3489969" target="_blank" >10.1109/MSP.2024.3489969</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Lagrangian Grid-Based Filters With Application to Terrain-Aided Navigation
Popis výsledku v původním jazyce
The column focuses on the state estimation of discrete-time stochastic dynamic systems from noisy or incomplete measurements. State estimation has been a subject of considerable research interest for the last decades. It plays an important role in e.g. navigation, tracking, speech and image processing, fault detection, and optimal control. In this column, we introduce and explain the recent state-of-the-art efficient grid-based filtering techniques that were proven to rival the ubiquitous particle filters based on the Monte Carlo integration in terms of performance and computational complexity. Compared to the particle filters, the grid-based filters provide deterministic results with improved resilience against initialisation error and measurement outliers. The readers are guided through the design of the grid-based filters within the scope of terrain-aided navigation, which is a topical navigation solution due to the latest jamming and spoofing attacks on global navigation satellite systems. The presented algorithms and related codes in MATLAB and Python are made publicly available together with the real-world measured dataset.
Název v anglickém jazyce
Lagrangian Grid-Based Filters With Application to Terrain-Aided Navigation
Popis výsledku anglicky
The column focuses on the state estimation of discrete-time stochastic dynamic systems from noisy or incomplete measurements. State estimation has been a subject of considerable research interest for the last decades. It plays an important role in e.g. navigation, tracking, speech and image processing, fault detection, and optimal control. In this column, we introduce and explain the recent state-of-the-art efficient grid-based filtering techniques that were proven to rival the ubiquitous particle filters based on the Monte Carlo integration in terms of performance and computational complexity. Compared to the particle filters, the grid-based filters provide deterministic results with improved resilience against initialisation error and measurement outliers. The readers are guided through the design of the grid-based filters within the scope of terrain-aided navigation, which is a topical navigation solution due to the latest jamming and spoofing attacks on global navigation satellite systems. The presented algorithms and related codes in MATLAB and Python are made publicly available together with the real-world measured dataset.
Klasifikace
Druh
J<sub>imp</sub> - Článek v periodiku v databázi Web of Science
CEP obor
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OECD FORD obor
20205 - Automation and control systems
Návaznosti výsledku
Projekt
<a href="/cs/project/EH22_008%2F0004590" target="_blank" >EH22_008/0004590: Robotika a pokročilá průmyslová výroba</a><br>
Návaznosti
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Ostatní
Rok uplatnění
2025
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
IEEE Signal Processing Magazine
ISSN
1053-5888
e-ISSN
1558-0792
Svazek periodika
42
Číslo periodika v rámci svazku
2
Stát vydavatele periodika
US - Spojené státy americké
Počet stran výsledku
7
Strana od-do
98-104
Kód UT WoS článku
001550524000010
EID výsledku v databázi Scopus
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