Positioning and Navigation Using Machine Learning Methods

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Éditeur :

Springer

Paru le : 2024-09-17

This is the first book completely dedicated to positioning and navigation using machine learning methods. It deals with ground, aerial, and space positioning and navigation for pedestrians, vehicles, UAVs, and LEO satellites. Most of the major machine learning methods are utilized, including supervi...
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À propos

Auteur

Éditeur

Collection
n.c

Parution
2024-09-17

Pages
374 pages

EAN papier
9789819761982

Auteur(s) du livre


Kegen Yu is currently Distinguished Professor in the School of Environment Science and Spatial Informatics at China University of Mining and Technology (CUMT), Xuzhou, China. He received his bachelor's degree from Changchun Geological College (now Jilin University), Changchun, China, in 1983; master’s degree from Australian National University, Canberra, Australia, in 1999; and Ph.D. degree from the University of Sydney, Australia, in 2003. He then participated in a number of research and development projects, as Task Leader or Principal Investigator in various institutions in Finland, Australia, and China, including Pervasive Ultra-wideband Low Spectral Energy Radio Systems (PULSERS), SAR Formation Flying (Garada), GNSS-R-based Ground Snow Water Equivalent Measurement, GNSS-R based Sea Rainfall Intensity Retrieval, etc. Prof. Yu's research focuses on the fields of positioning, navigation, and remote sensing. Prof. Yu was awarded Hubei Provincial “One Hundred Talents Program” and received the honor of Distinguished Expert of Hubei Province in 2015. He has co-authored 6 books and more than 150 journal papers. He is also Senior Member of IEEE and Member of Navigation Systems Panel of IEEE AESS. He was ranked in the world's top 2% scientists list in 2022 by Stanford University and Elsevier.

Caractéristiques détaillées - droits

EAN PDF
9789819761999
Prix
189,89 €
Nombre pages copiables
3
Nombre pages imprimables
37
Taille du fichier
26767 Ko
EAN EPUB
9789819761999
Prix
189,89 €
Nombre pages copiables
3
Nombre pages imprimables
37
Taille du fichier
76186 Ko

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