Kalman Filtering and Information Fusion / / by Hongbin Ma, Liping Yan, Yuanqing Xia, Mengyin Fu

• Autore: Ma Hongbin
• Pubblicazione: Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2020
• Edizione: 1st ed. 2020.
• Descrizione fisica: 1 online resource (xvii, 291 pages) : illustrations
• Disciplina: 629.8312
• Soggetto topico: Automatic control; Robotics; Automation; Engineering mathematics; Engineering - Data processing; System theory; Control theory; Electrical engineering; Control, Robotics, Automation; Mathematical and Computational Engineering Applications; Systems Theory, Control; Electrical and Electronic 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