Yan Liping (49 results)

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  • Language: English

    Published by Springer, 2020

    9811594252 / 9789811594250

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  • Language: English

    Published by Springer, 2021

    9811594287 / 9789811594281

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  • Language: English

    Published by Springer, 2020

    9811508089 / 9789811508080

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  • Language: English

    Published by Springer, 2019

    9811508054 / 9789811508059

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  • Language: English

    Published by Springer, 2020

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  • Language: English

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    9811594287 / 9789811594281

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  • Language: English

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  • Language: English

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  • Language: English

    Published by Springer, 2021

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    Condition: New. 1st ed. 2021 edition NO-PA16APR2015-KAP.

  • Language: English

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    Condition: New. 1st ed. 2021 edition NO-PA16APR2015-KAP.

  • Language: English

    Published by Springer, 2021

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    Taschenbuch. Condition: Neu. Multisensor Fusion Estimation Theory and Application | Liping Yan (u. a.) | Taschenbuch | xvii | Englisch | 2021 | Springer | EAN 9789811594281 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

  • Language: English

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    Condition: New. 1st ed. 2020 edition NO-PA16APR2015-KAP.

  • Language: English

    Published by Springer, 2019

    9811508054 / 9789811508059

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  • Language: English

    Published by Springer Nature, 2020

    9811594252 / 9789811594250

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    Hardcover. Condition: Brand New. 244 pages. 9.25x6.10x0.75 inches. In Stock.

  • Language: English

    Published by Springer Nature, 2020

    9811508089 / 9789811508080

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    Paperback. Condition: Brand New. 312 pages. 9.25x6.10x0.67 inches. In Stock.

  • Language: English

    Published by Springer, 2021

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    Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.

  • Language: English

    Published by Springer, 2020

    9811594252 / 9789811594250

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    Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.

  • Language: English

    Published by Springer, 2019

    9811508054 / 9789811508059

    • Hardcover

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    Hardcover. Condition: Brand New. 312 pages. 9.25x6.10x0.94 inches. In Stock.

  • Language: English

    Published by Springer, 2019

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    Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.

  • Language: English

    Published by Springer, 2020

    9811508089 / 9789811508080

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    Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.

  • Language: English

    Published by Springer, 2019

    9811346364 / 9789811346361

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  • Language: English

    Published by Springer, 2018

    9811314586 / 9789811314582

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    Condition: New. pp. 745.

  • Language: English

    Published by Springer, 2019

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  • Language: English

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  • Language: English

    Published by Springer, 2021

    9811594287 / 9789811594281

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  • Language: English

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  • Language: English

    Published by Springer Nature Singapore Nov 2020, 2020

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    Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 248 pp. Englisch.

  • Language: English

    Published by Springer Nature Singapore Nov 2021, 2021

    9811594287 / 9789811594281

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    Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermanyBuchWeltWeit Ludwig Meier e.K.

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    Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 248 pp. Englisch.

  • Language: English

    Published by Springer, Berlin|Springer Nature Singapore|Springer, 2021

    9811594287 / 9789811594281

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    Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book focuses on the basic theory and methods of multisensor data fusion state estimation and its application. In Part II, the data fusion state estimation algorithms under networked environment are introduced. Part III consists of three chapters, in wh.