Multisensor Fusion Estimation Theory and Application
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Beschreibung
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.
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.
Addresses multisensor data fusion state estimation both in theory and practice Studies comprehensively state fusion estimation methods in case of networked transmission problems Presents the design of event-triggered strategies and the state fusion estimation algorithms in case of event-triggered conditions Provides in-depth state fusion estimation algorithms in case of non-Gaussian but heavy-tailed noises, including the information filter for heavy-tailed systems
Autor*in
Liping Yan
Themen in »Multisensor Fusion Estimation Theory and Application«
Multisensor data fusion State estimation Kalman filter Heavy-tailed noise Event-triggered mechanism