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Kalman filtering and information fusion

Ma, Hongbin.

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Kalman filtering and information fusion

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Kalman filtering and information fusion/ by Hongbin Ma ... [et al.].
其他作者:	Ma, Hongbin.
出版者:	Singapore :Springer Singapore : : 2020.,
面頁冊數:	xvii, 291 p. :ill. (some col.), digital ;24 cm.
內容註:	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.
Contained By:	Springer eBooks
標題:	Kalman filtering. -
電子資源:	https://doi.org/10.1007/978-981-15-0806-6
ISBN:	9789811508066

Kalman filtering and information fusion
Kalman filtering and information fusion[electronic resource] /by Hongbin Ma ... [et al.]. - Singapore :Springer Singapore :2020. - xvii, 291 p. :ill. (some col.), digital ;24 cm.

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.

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.

ISBN: 9789811508066

Standard No.: 10.1007/978-981-15-0806-6doiSubjects--Topical Terms:

544465
Kalman filtering.

LC Class. No.: QA702.3

Dewey Class. No.: 629.8

Kalman filtering and information fusion
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505 0 $a 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.
520 $a 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.
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