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

Kegen Yu,Jindrich Duník,Michael S. Braasch,Pau Closas,Fabio Dovis

IEEE Trans. Aerosp. Electron. Syst.（2024）

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Abstract

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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Key words

Machine Learning Methods,Position In Space,Complex Environment,Local System,Local Services,Positioning System,Harsh Environments,Ergogenic,Aerial Vehicles,Global Navigation Satellite System,Local Position,Wide Range Of Fields,Neural Network,Deep Learning,Learning Algorithms,Convolutional Neural Network,Support Vector Machine,Unsupervised Learning,K-nearest Neighbor,Unmanned Aerial Vehicles,Position Estimation,Pose Estimation,Navigation Accuracy,Graph Convolutional Network,Back Propagation Neural Network,Navigation System,Inertial Measurement Unit,Wireless Local Area Network,WiFi Signals

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