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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.
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.
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.
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