Springer Multisensor Fusion Estimation Theory and Application

Product Description 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. From the Back Cover 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. About the Author Liping Yan was born in Henan Province, China, in 1979. She received her B.S. degree and M.S. degree both in Mathematics from Henan University, Kaifeng city, Henan Province, P. R. China, in 2000 and 2003, respectively, and received her Ph.D. degree in Control Science and Engineering from Tsinghua University, Beijing, China, in 2007. From January 2007 to July 2009, she was a Postdoctoral Research Associate in the Equipment Academy of Airforce. Since July 2009, she has been with the School of Automatic Control, Beijing Institute of Technology, Beijing, first as an Assistant Professor, then, since 2011, as an Associate Professor. From March 2012 to March 2013, supported by CSC, she was a Visiting Scholar in the University of New Orleans, New Orleans, LA, USA. From September 2018 to August 2019, supported by CSC, she was a Visiting Scholar in the University of Windsor, Windsor, ON, Canada. She has co-authored five books and more than 60 journal and conference papers. Currently, she is an Associate Professor and Ph.D. supervisor in BIT. Her research interests include multisensor data fusion, state estimation, image registration, intelligent navigation and integrated navigation, etc. Lu Jiang was born in Shandong Province, China, in 1990. She received her B.S. degree in Automation from Qingdao University, Qingdao city, Shandong Province, P. R. China, in 2013, and received her Ph.D. degree in Control Science and Engineering from Beijing Institute of Technology, Beijing, China, in 2019. She is currently an Assistant Professor in Beijing Technology and Business University. Her research interests include multisensor data fusion, optimal state estimation, etc. Yuanqing Xia was born in Anhui Province, China, in 1971, and graduated from the Department of Mathematics, Chuzhou University, Chuzhou, China, in 1991. He received his M.S. degree in Fundamental Mathematics from Anhui University, China, in 1998, and his Ph.D. degree in Control Theory and Control Engineering from Beijing University of Aeronautics and Astronautics, Beijing, C