Guest Editorial for the TAES Special Section on Machine Learning Methods for Aerial and Space Positioning and Navigation

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F24%3A43975077" target="_blank" >RIV/49777513:23520/24:43975077 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/TAES.2024.3385216" target="_blank" >https://doi.org/10.1109/TAES.2024.3385216</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TAES.2024.3385216" target="_blank" >10.1109/TAES.2024.3385216</a>

Result language
angličtina
Original language name
Guest Editorial for the TAES Special Section on Machine Learning Methods for Aerial and Space Positioning and Navigation
Original language description
Positioning and navigation plays a significant role in a wide range of fields, such as aerospace, defense, and transportation, especially due to the continuous performance enhancement of the four Global Navigation Satellite Systems (GNSS) [1], [2] and the advent of complementary local positioning systems [3], [4]. Nowadays, requirements on positioning and navigation are becoming stricter in areas such as reliability, accuracy, continuity, complexity, integrability, and safety to enable better location-based services. In many complex and harsh environments, it is still a demanding task (such as for aerial and space vehicles) to generate real-time valid location information and perform the desired navigation, which enables to fulfill the assigned duties [5].
Czech name
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Czech description
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Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
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OECD FORD branch
20205 - Automation and control systems

Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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