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Multisensor fusion estimation theory...

Jiang, Lu.

Multisensor fusion estimation theory and application

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Multisensor fusion estimation theory and applicationby Liping Yan, Lu Jiang, Yuanqing Xia.
作者:	Yan, Liping.
其他作者:	Jiang, Lu.
出版者:	Singapore :Springer Singapore :2021.
面頁冊數:	xvii, 227 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Multisensor data fusion.
電子資源:	https://doi.org/10.1007/978-981-15-9426-7
ISBN:	9789811594267$q(electronic bk.)

Multisensor fusion estimation theory and application
Yan, Liping.

Multisensor fusion estimation theory and application[electronic resource] /by Liping Yan, Lu Jiang, Yuanqing Xia. - Singapore :Springer Singapore :2021. - xvii, 227 p. :ill. (some col.), digital ;24 cm.

Introduction to Optimal Fusion Estimation and Kalman Filtering: Preliminaries -- Kalman Filtering of Discrete Dynamic Systems -- Optimal Kalman filtering Fusion for Linear Dynamic Systems with Cross-Correlated Sensor Noises -- Distributed Data Fusion for Multirate Sensor Networks -- Optimal Estimation for Multirate Systems with Unreliable Measurements and Correlated Noise -- Fusion Estimation for Asynchronous Multirate Multisensor Systems with Unreliable Measurements and Coupled Noises -- Multi-sensor Distributed Fusion Estimation for Systems with Network Delays, Uncertainties and Correlated Noises -- Event-triggered Centralized Fusion Estimation for Dynamic Systems with Correlated Noises -- Event-triggered Distributed Fusion Estimation for WSN Systems -- Event-triggered Sequential Fusion Estimation for Dynamic Systems with Correlated Noises -- Distributed Fusion Estimation for Multisensor Systems with Heavy-tailed Noises -- Sequential Fusion Estimation for Multisensor Systems with Heavy-tailed Noises.

This book focuses on the basic theory and methods of multisensor data fusion state estimation and its application. It consists of four parts with 12 chapters. In Part I, the basic framework and methods of multisensor optimal estimation and the basic concepts of Kalman filtering are briefly and systematically introduced. In Part II, the data fusion state estimation algorithms under networked environment are introduced. Part III consists of three chapters, in which the fusion estimation algorithms under event-triggered mechanisms are introduced. Part IV consists of two chapters, in which fusion estimation for systems with non-Gaussian but heavy-tailed noises are introduced. The book is primarily intended for researchers and engineers in the field of data fusion and state estimation. It also benefits for both graduate and undergraduate students who are interested in target tracking, navigation, networked control, etc.

ISBN: 9789811594267$q(electronic bk.)

Standard No.: 10.1007/978-981-15-9426-7doiSubjects--Topical Terms:

182455
Multisensor data fusion.

LC Class. No.: TK7872.D48 / Y35 2021

Dewey Class. No.: 681.2

Multisensor fusion estimation theory and application
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505 0 $a Introduction to Optimal Fusion Estimation and Kalman Filtering: Preliminaries -- Kalman Filtering of Discrete Dynamic Systems -- Optimal Kalman filtering Fusion for Linear Dynamic Systems with Cross-Correlated Sensor Noises -- Distributed Data Fusion for Multirate Sensor Networks -- Optimal Estimation for Multirate Systems with Unreliable Measurements and Correlated Noise -- Fusion Estimation for Asynchronous Multirate Multisensor Systems with Unreliable Measurements and Coupled Noises -- Multi-sensor Distributed Fusion Estimation for Systems with Network Delays, Uncertainties and Correlated Noises -- Event-triggered Centralized Fusion Estimation for Dynamic Systems with Correlated Noises -- Event-triggered Distributed Fusion Estimation for WSN Systems -- Event-triggered Sequential Fusion Estimation for Dynamic Systems with Correlated Noises -- Distributed Fusion Estimation for Multisensor Systems with Heavy-tailed Noises -- Sequential Fusion Estimation for Multisensor Systems with Heavy-tailed Noises.
520 $a This book focuses on the basic theory and methods of multisensor data fusion state estimation and its application. It consists of four parts with 12 chapters. In Part I, the basic framework and methods of multisensor optimal estimation and the basic concepts of Kalman filtering are briefly and systematically introduced. In Part II, the data fusion state estimation algorithms under networked environment are introduced. Part III consists of three chapters, in which the fusion estimation algorithms under event-triggered mechanisms are introduced. Part IV consists of two chapters, in which fusion estimation for systems with non-Gaussian but heavy-tailed noises are introduced. The book is primarily intended for researchers and engineers in the field of data fusion and state estimation. It also benefits for both graduate and undergraduate students who are interested in target tracking, navigation, networked control, etc.
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