Optimal Estimation And Information Fusion Theory And Algorithms
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Optimal Estimation and Information Fusion: Theory and Algorithms
This book mainly focuses on the theme of optimizing estimation and sensor information fusion processing for stochastic dynamic systems. It summarizes the basic theories and methods of optimizing estimation and information fusion direction, including stochastic system models, optimal estimation methods, linear state estimation, nonlinear state estimation, information fusion models, structures, data processing methods, data association based on multi-source data estimation, and other aspects. On the basis of years of teaching practice, the author optimizes the content layout, focuses on the basic theoretical methods of the subject, emphasizes the systematic nature of the theory and the rigor of expression, selectively cuts out some outdated content, and introduces some important and widely accepted new developments in the subject. On the other hand, this book also serves as a reference material for technical developers in this field.
Advances in Multi-Sensor Information Fusion: Theory and Applications 2017
This book is a printed edition of the Special Issue "Advances in Multi-Sensor Information Fusion: Theory and Applications 2017" that was published in Sensors
Kalman Filtering and Information Fusion
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.