東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Kalman filtering and information fusion

Ma, Hongbin.

FindBook

Google Book

Amazon

博客來

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
LDR:02817nmm a2200337 a 4500 001 2214714
003 DE-He213
005 20200317145234.0
006 m d
007 cr nn 008maaau
008 201118s2020 si s 0 eng d
020 $a 9789811508066 $q (electronic bk.)
020 $a 9789811508059 $q (paper)
024 7 $a 10.1007/978-981-15-0806-6 $2 doi
035 $a 978-981-15-0806-6
040 $a GP $c GP
041 0 $a eng
050 4 $a QA702.3
072 7 $a TJFM $2 bicssc
072 7 $a TEC004000 $2 bisacsh
072 7 $a TJFM $2 thema
072 7 $a TJFD $2 thema
082 0 4 $a 629.8 $2 23
090 $a QA702.3 $b .K14 2020
245 0 0 $a Kalman filtering and information fusion $h [electronic resource] / $c by Hongbin Ma ... [et al.].
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2020.
300 $a xvii, 291 p. : $b ill. (some col.), digital ; $c 24 cm.
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.
650 0 $a Kalman filtering. $3 544465
650 1 4 $a Control, Robotics, Mechatronics. $3 1002220
650 2 4 $a Mathematical and Computational Engineering. $3 3226306
650 2 4 $a Systems Theory, Control. $3 893834
650 2 4 $a Electrical Engineering. $3 1001838
700 1 $a Ma, Hongbin. $3 2153251
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
856 4 0 $u https://doi.org/10.1007/978-981-15-0806-6
950 $a Intelligent Technologies and Robotics (Springer-42732)