Overview
Provides Kalman filters under information theoretic criteria to achieve excellent performance in a range of applications
Presents each chapter with a brief review of fundamentals and then focuses on the topic’s most important properties
Geared to students’ understanding of linear algebra, signal processing, and statistics
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Table of contents (8 chapters)
Keywords
About this book
Authors and Affiliations
About the authors
Lujuan Dang received the B.S. degree in information science and technology from Northwest University, Xi’an, China, in 2015, and the M.S. degree in electronic and information engineering from Southwest University, Chongqing, China, in 2018. She is currently pursuing the Ph.D. degree with the Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, Xi’an. Her current interests focus on adaptive filtering and information theoretic learning.
Nanning Zheng graduated from the Department of Electrical Engineering, Xi’an Jiaotong University, Xi’an, China, in 1975, and received the M.S. degree in information and control engineering from Xi’an Jiaotong University in 1981 and the Ph.D. degree in electrical engineering from Keio University, Yokohama, Japan, in 1985. He is currently a professor and director of the Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University. His research interests include computer vision, pattern recognition and image processing, and hardware implementation of intelligent systems. Prof. Zheng became a member of the Chinese Academy of Engineering in 1999, and he is the Chinese Representative on the Governing Board of the International Association for Pattern Recognition. He is an IEEE Fellow and serves as an executive deputy editor of the Chinese Science Bulletin.
Jose C. Principe is a Distinguished Professor of Electrical and Computer Engineering at the University of Florida where he teaches advanced signal processing, machine learning and artificial neural networks (ANNs). He is the Eckis Professor and the Founder and Director of the University of Florida Computational NeuroEngineering Laboratory (CNEL) www.cnel.ufl.edu. The CNEL Lab innovated signal and pattern recognition principles based on information theoretic criteria, as well as filtering in functional spaces. His secondary area of interest has focused in applications to computational neuroscience, Brain Machine Interfaces and brain dynamics. Dr. Principe is a Fellow of the AAAS, IEEE, NAI, AIMBE, and IAMBE. He received the Gabor Award from the INNS, the Shannon- Nyquist Technical Achievement Award from the IEEE Signal Processing Society, the Career Achievement Award from the IEEE EMBS and the Neural Network Pioneer Award of the IEEE CIS. He has more than 33 patents awarded and over 900 publications in the areas of adaptive signal processing, control of nonlinear dynamical systems, machine learning and neural networks, information theoretic learning, with applications to neurotechnology and brain computer interfaces. He directed 108 Ph.D. dissertations and 65 Master theses. He has received four Honorary Doctor Degrees, from Finland, Italy, Brazil and Colombia, and routinely serves in international scientific advisory boards of Universities and Companies.
Bibliographic Information
Book Title: Kalman Filtering Under Information Theoretic Criteria
Authors: Badong Chen, Lujuan Dang, Nanning Zheng, Jose C. Principe
DOI: https://doi.org/10.1007/978-3-031-33764-2
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023
Hardcover ISBN: 978-3-031-33763-5Published: 19 August 2023
Softcover ISBN: 978-3-031-33766-6Due: 02 September 2024
eBook ISBN: 978-3-031-33764-2Published: 18 August 2023
Edition Number: 1
Number of Pages: XV, 294
Number of Illustrations: 3 b/w illustrations, 73 illustrations in colour
Topics: Signal, Image and Speech Processing, Mathematical Methods in Physics, Theoretical, Mathematical and Computational Physics, Economic Theory/Quantitative Economics/Mathematical Methods, Mathematical and Computational Engineering, Artificial Intelligence