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Autore: |
Ma Hongbin
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Titolo: |
Kalman Filtering and Information Fusion / / by Hongbin Ma, Liping Yan, Yuanqing Xia, Mengyin Fu
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Pubblicazione: | Singapore : , : Springer Singapore : , : Imprint : Springer, , 2020 |
Edizione: | 1st ed. 2020. |
Descrizione fisica: | 1 online resource (xvii, 291 pages) : illustrations |
Disciplina: | 629.8312 |
Soggetto topico: | Control engineering |
Robotics | |
Mechatronics | |
Applied mathematics | |
Engineering mathematics | |
System theory | |
Electrical engineering | |
Control, Robotics, Mechatronics | |
Mathematical and Computational Engineering | |
Systems Theory, Control | |
Electrical Engineering | |
Persona (resp. second.): | YanLiping |
XiaYuanqing | |
FuMengyin | |
Nota di bibliografia: | Includes bibliographical references. |
Nota di contenuto: | 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. |
Sommario/riassunto: | 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. |
Titolo autorizzato: | Kalman Filtering and Information Fusion ![]() |
ISBN: | 981-15-0806-2 |
Formato: | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione: | Inglese |
Record Nr.: | 9910484519603321 |
Lo trovi qui: | Univ. Federico II |
Opac: | Controlla la disponibilità qui |