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].


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    Titel :

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


    Beteiligte:
    Yu, Kegen (Autor:in) / Dunik, Jindrich (Autor:in) / Braasch, Michael S. (Autor:in) / Closas, Pau (Autor:in) / Dovis, Fabio (Autor:in)


    Erscheinungsdatum :

    2024-06-01


    Format / Umfang :

    84429 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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