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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of the subject, emphasizes the systematic nature of the theory and the rigor of expression, selectively cuts out some outdated content, and introduces some important and widely accepted new developments in the subject.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.
List of contents
Overview of Stochastic Systems and Basic Concepts of Estimation.- Linear Estimation of Static Systems.- Linear Dynamic Systems with Random Inputs.- State Estimation for Discrete Time Linear Systems.- Extension of State Estimation for Discrete Time Linear Systems.- State Estimation for Discrete Time Nonlinear Systems.- Extended Kalman Filtering Unscented Transform Kalman Filtering.- Particle Filtering.- Theory and Methods of Data Association.- Maneuvering Target Tracking Theory and Methods.- Information Fusion Concepts, Models, and Structures.- Information Fusion Methods.- Application Examples: Algorithms, Simulation, and Analysis.
About the author
Dr. Ming Lei received his doctoral degree in the discipline of control theory and control engineering from Xi'an Jiao Tong University (XJTU) in 2006. He then conducted postdoctoral research and visiting research fellow at the University of Bordeaux I and METRO-FRANCE in France from 2008 to 2012. He is currently an associate professor at Shanghai Jiao Tong University (SJTU). His main research interests include stochastic estimation and nonlinear filtering, maneuvering multi-target tracking and multi-source information fusion, and their applications in maneuvering target detection, unmanned aerial vehicle guidance and control.
Summary
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.
