Machine Learning for Indoor Localization and Navigation

Language: English

Published by Springer, Springer Jul 2024, 2024

3031267141 / 9783031267147

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This item is printed on demand - Print on Demand Titel. Neuware -While GPS is the de-facto solution for outdoor positioning with a clear sky view, there is no prevailing technology for GPS-deprived areas, including dense city centers, urban canyons, buildings and other covered structures, and subterranean facilities such as underground mines, where GPS signals are severely attenuated or totally blocked. As an alternative to GPS for the outdoors, indoor localization using machine learning is an emerging embedded and Internet of Things (IoT) application domain that is poised to reinvent the way we navigate in various indoor environments. This book discusses advances in the applications of machine learning that enable the localization and navigation of humans, robots, and vehicles in GPS-deficient environments. The book explores key challenges in the domain, such as mobile device resource limitations, device heterogeneity, environmental uncertainties, wireless signal variations, and security vulnerabilities. Countering these challenges can improve theaccuracy, reliability, predictability, and energy-efficiency of indoor localization and navigation. The book identifies severalnovel energy-efficient, real-time, and robust indoor localization techniques that utilize emerging deep machine learning and statistical techniques to address the challenges for indoor localization and navigation.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 584 pp. Englisch.

Seller Inventory # 9783031267147

Title
Machine Learning for Indoor Localization and Navigation
Author
Saideep Tiku
Publisher
Springer, Springer Jul 2024
Publication year
2024
Condition
Neu
Binding
Taschenbuch
Language
English
ISBN 10
3031267141
ISBN 13
9783031267147
Item weight
873 grams
Dimensions
235x155x32 mm

buchversandmimpf2000

Emtmannsberg, BAYE, Germany

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