Kalman Filtering Under Information Theoretic Criteria - Chen, Badong;Dang, Lujuan;Zheng, Nanning
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This book provides several efficient Kalman filters (linear or nonlinear) under information theoretic criteria. They achieve excellent performance in complicated non-Gaussian noises with low computation complexity and have great practical application potential. The book combines all these perspectives and results in a single resource for students and practitioners in relevant application fields. Each chapter starts with a brief review of fundamentals, presents the material focused on the most important properties and evaluates comparatively the models discussing free parameters and their…mehr

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Produktbeschreibung
This book provides several efficient Kalman filters (linear or nonlinear) under information theoretic criteria. They achieve excellent performance in complicated non-Gaussian noises with low computation complexity and have great practical application potential. The book combines all these perspectives and results in a single resource for students and practitioners in relevant application fields. Each chapter starts with a brief review of fundamentals, presents the material focused on the most important properties and evaluates comparatively the models discussing free parameters and their effect on the results. Proofs are provided at the end of each chapter. The book is geared to senior undergraduates with a basic understanding of linear algebra, signal processing and statistics, as well as graduate students or practitioners with experience in Kalman filtering.

Autorenporträt
Badong Chen received the B.S. and M.S. degrees in Control Theory and Engineering from Chongqing University, Chongqing, China, in 1997 and 2003, respectively, and the Ph.D. degree in Computer Science and Technology from Tsinghua University, Beijing, China, in 2008. He was a Postdoctoral Associate at the University of Florida Computational NeuroEngineering Laboratory (CNEL) from 2010 to 2012. He visited the Nanyang Technological University (NTU), Singapore, as a visiting research scientist in 2015. He also served as a senior research fellow with The Hong Kong Polytechnic University in 2017. Currently he is a professor at the Institute of Artificial Intelligence and Robotics (IAIR), Xi'an Jiaotong University, Xi'an, China. His research interests are in signal processing, machine learning, artificial intelligence, neural engineering and robotics. He has published two books and over 200 papers in various journals and conference proceedings and his papers have got over 5500 citations according to Google Scholar. Dr. Chen is an IEEE Senior Member, a Technical Committee Member of IEEE SPS Machine Learning for Signal Processing (MLSP) and IEEE CIS Cognitive and Developmental Systems (CDS), and an associate editor of IEEE Transactions on Cognitive and Developmental Systems, IEEE Transactions on Neural Networks and Learning Systems and Journal of The Franklin Institute and has been on the editorial board of Entropy.     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.
Rezensionen
"The book is well-suited for senior undergraduates who possess a foundational understanding of linear algebra, signal processing, and statistics. Additionally, it caters to graduate students or professionals who have practical experience in Kalman filtering." (Qi Lu, zbMATH 1532.93002, 2024)