國立高雄大學圖資館 |

Language: English

Back

Positioning and navigation using machine learning methods

Record Type:	Electronic resources : Monograph/item
Title/Author:	Positioning and navigation using machine learning methodsedited by Kegen Yu.
other author:	Yu, Kegen.
Published:	Singapore :Springer Nature Singapore :2024.
Description:	x, 374 p. :ill. (chiefly color), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Wireless localization.
Online resource:	https://doi.org/10.1007/978-981-97-6199-9
ISBN:	9789819761999$q(electronic bk.)

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.5..

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$q(electronic bk.)

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

775723
Wireless localization.

LC Class. No.: TK5103.4895

Dewey Class. No.: 621.3981

Positioning and navigation using machine learning methods
LDR:02669nmm a2200337 a 4500 001 669389
003 DE-He213
005 20240917130553.0
006 m d
007 cr nn 008maaau
008 250120s2024 si s 0 eng d
020 $a 9789819761999$q(electronic bk.)
020 $a 9789819761982$q(paper)
024 7 $a 10.1007/978-981-97-6199-9 $2 doi
035 $a 978-981-97-6199-9
040 $a GP $c GP
041 0 $a eng
050 4 $a TK5103.4895
072 7 $a TJK $2 bicssc
072 7 $a TEC041000 $2 bisacsh
072 7 $a TJK $2 thema
082 0 4 $a 621.3981 $2 23
090 $a TK5103.4895 $b .P855 2024
245 0 0 $a Positioning and navigation using machine learning methods $h [electronic resource] / $c edited by Kegen Yu.
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2024.
300 $a x, 374 p. : $b ill. (chiefly color), digital ; $c 24 cm.
490 1 $a Navigation: science and technology, $x 2522-0462 ; $v v. 14
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.
650 0 $a Wireless localization. $3 775723
650 0 $a Navigation. $3 226293
650 0 $a Machine learning. $3 188639
650 1 4 $a Communications Engineering, Networks. $3 273745
650 2 4 $a Machine Learning. $3 833608
650 2 4 $a Signal, Speech and Image Processing. $3 919570
700 1 $a Yu, Kegen. $3 829765
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a Navigation: science and technology ; $v v.5. $3 859072
856 4 0 $u https://doi.org/10.1007/978-981-97-6199-9
950 $a Engineering (SpringerNature-11647)