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Positioning and navigation using machine learning methods

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Positioning and navigation using machine learning methods/ edited by Kegen Yu.
其他作者:	Yu, Kegen.
出版者:	Singapore :Springer Nature Singapore : : 2024.,
面頁冊數:	x, 374 p. :ill. (chiefly color), digital ; : 24 cm.;
Contained By:	Springer Nature eBook
標題:	Wireless localization. -
電子資源:	https://doi.org/10.1007/978-981-97-6199-9
ISBN:	9789819761999

Positioning and navigation using machine learning methods
Positioning and navigation using machine learning methods[electronic resource] /edited by Kegen Yu. - Singapore :Springer Nature Singapore :2024. - x, 374 p. :ill. (chiefly color), digital ;24 cm. - Navigation: science and technology,v. 142522-0462 ;. - Navigation: science and technology ;v. 14..

Chapter 1. Introduction -- Chapter 2. Indoor localization using ranging model constructed with BP neural network -- Chapter 3. Classification of signal propagation channel using CNN and wavelet packet analysis -- Chapter 4. Semi supervised indoor localization -- Chapter 5. Unsupervised learning for practical indoor localization -- Chapter 6. Deep learning based PDR localization using smartphone sensors and GPS data -- Chapter 7. Deductive reinforcement learning for vehicle navigation -- Chapter 8. Privacy preserving aggregation for federated learning based navigation -- Chapter 9. Learning enhanced INS/GPS integrated navigation -- Chapter 10. UAV localization using deep supervised learning and reinforcement learning -- Chapter 11. Learning based UAV path planning with collision avoidance -- Chapter 12. Learning assisted navigation for planetary rovers -- Chapter 13. Improved planetary rover localization using slip based autonomous ZUPT.

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 supervised learning, unsupervised learning, deep learning, and reinforcement learning. The book presents both fundamentals and in-depth studies as well as practical examples in positioning and navigation. Extensive data processing and experimental results are provided in the major chapters through conducting experimental campaigns or using in-situ measurements.

ISBN: 9789819761999

Standard No.: 10.1007/978-981-97-6199-9doiSubjects--Topical Terms:

1140094
Wireless localization.

LC Class. No.: TK5103.4895

Dewey Class. No.: 621.3981

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505 0 $a Chapter 1. Introduction -- Chapter 2. Indoor localization using ranging model constructed with BP neural network -- Chapter 3. Classification of signal propagation channel using CNN and wavelet packet analysis -- Chapter 4. Semi supervised indoor localization -- Chapter 5. Unsupervised learning for practical indoor localization -- Chapter 6. Deep learning based PDR localization using smartphone sensors and GPS data -- Chapter 7. Deductive reinforcement learning for vehicle navigation -- Chapter 8. Privacy preserving aggregation for federated learning based navigation -- Chapter 9. Learning enhanced INS/GPS integrated navigation -- Chapter 10. UAV localization using deep supervised learning and reinforcement learning -- Chapter 11. Learning based UAV path planning with collision avoidance -- Chapter 12. Learning assisted navigation for planetary rovers -- Chapter 13. Improved planetary rover localization using slip based autonomous ZUPT.
520 $a 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 supervised learning, unsupervised learning, deep learning, and reinforcement learning. The book presents both fundamentals and in-depth studies as well as practical examples in positioning and navigation. Extensive data processing and experimental results are provided in the major chapters through conducting experimental campaigns or using in-situ measurements.
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