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- Author: Liping Yan
- Publisher: Springer
- ISBN-10: 9811594252
- ISBN-13: 9789811594250
- Format: 15.6 x 23.4 x 1.6 cm, kieti viršeliai
- Language: English
- SAVE -10% with code: EXTRA
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Description
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.
EXTRA 10 % discount with code: EXTRA
The promotion ends in 22d.20:01:12
The discount code is valid when purchasing from 10 €. Discounts do not stack.
- Author: Liping Yan
- Publisher: Springer
- ISBN-10: 9811594252
- ISBN-13: 9789811594250
- Format: 15.6 x 23.4 x 1.6 cm, kieti viršeliai
- Language: English English
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.
Reviews