| Vorliegende Sprache |
eng |
| ISBN |
978-981-15-0805-9 |
| Name |
Ma, Hongbin ¬[VerfasserIn]¬ |
| Yan, Liping ¬[VerfasserIn]¬ |
| Name ANZEIGE DER KETTE |
Yan, Liping ¬[VerfasserIn]¬ |
| Name |
Xia, Yuanqing ¬[VerfasserIn]¬ |
| T I T E L |
Kalman Filtering and Information Fusion |
| Auflage |
1st ed. 2020 |
| Verlagsort |
Singapore |
| Verlag |
Springer |
| Erscheinungsjahr |
2020 |
| 2020 |
| Umfang |
1 Online-Ressource (XVII, 291 p. 101 illus., 38 illus. in color) |
| Reihe |
Springer eBooks. Intelligent Technologies and Robotics |
| Titelhinweis |
Erscheint auch als (Druck-Ausgabe)ISBN: 978-981-15-0805-9 |
| ISBN |
ISBN 978-981-15-0806-6 |
| Klassifikation |
TJFM |
| TJFD |
| TJFM |
| TEC004000 |
| 629.8 |
| TJ210.2-211.495 |
| TJ163.12 |
| Kurzbeschreibung |
Preface -- Part I Kalman Filtering: Preliminaries -- Part II Kalman Filtering for Uncertain Systems -- Part III Kalman Filtering for Multi-Sensor Systems -- Part IV Kalman Filtering for Multi-Agent Systems |
| 2. Kurzbeschreibung |
This book addresses a key technology for digital information processing: Kalman filtering, which is generally considered to be one of the greatest discoveries of the 20th century. It introduces readers to issues concerning various uncertainties in a single plant, and to corresponding solutions based on adaptive estimation. Further, it discusses in detail the issues that arise when Kalman filtering technology is applied in multi-sensor systems and/or multi-agent systems, especially when various sensors are used in systems like intelligent robots, autonomous cars, smart homes, smart buildings, etc., requiring multi-sensor information fusion techniques. Furthermore, when multiple agents (subsystems) interact with one another, it produces coupling uncertainties, a challenging issue that is addressed here with the aid of novel decentralized adaptive filtering techniques. Overall, the book’s goal is to provide readers with a comprehensive investigation into the challenging problem of making Kalman filtering work well in the presence of various uncertainties and/or for multiple sensors/components. State-of-art techniques are introduced, together with a wealth of novel findings. As such, it can be a good reference book for researchers whose work involves filtering and applications; yet it can also serve as a postgraduate textbook for students in mathematics, engineering, automation, and related fields. To read this book, only a basic grasp of linear algebra and probability theory is needed, though experience with least squares, navigation, robotics, etc. would definitely be a plus |
| SWB-Titel-Idn |
1684975247 |
| Signatur |
Springer E-Book |
| Bemerkungen |
Elektronischer Volltext - Campuslizenz |
| Elektronische Adresse |
$uhttps://doi.org/10.1007/978-981-15-0806-6 |
| Internetseite / Link |
Resolving-System |
