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].
Guest Editorial for the TAES Special Section on Machine Learning Methods for Aerial and Space Positioning and Navigation
IEEE Transactions on Aerospace and Electronic Systems ; 60 , 3 ; 2494-2499
2024-06-01
84429 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Special Section Guest Editorial
British Library Online Contents | 2008
|Special section guest editorial
British Library Online Contents | 2001
|Special section guest editorial
British Library Online Contents | 2001
|Special section guest editorial
British Library Online Contents | 2001
|