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＜E-Book＞
Kalman Filtering Under Information Theoretic Criteria / by Badong Chen, Lujuan Dang, Nanning Zheng, Jose C. Principe

Edition	1st ed. 2023.
Publisher	(Cham : Springer International Publishing : Imprint: Springer)
Year	2023
Language	English
Size	XV, 294 p. 76 illus., 73 illus. in color : online resource
Authors	*Chen, Badong author Dang, Lujuan author Zheng, Nanning author Principe, Jose C author SpringerLink (Online service)
Subjects	LCSH:Signal processing LCSH:Mathematical physics LCSH:Econometrics LCSH:Engineering mathematics LCSH:Engineering -- Data processing  All Subject Search LCSH:Artificial intelligence FREE:Signal, Speech and Image Processing FREE:Mathematical Methods in Physics FREE:Theoretical, Mathematical and Computational Physics FREE:Quantitative Economics FREE:Mathematical and Computational Engineering Applications FREE:Artificial Intelligence
Notes	Chapter 1. Introduction -- Chapter 2. Kalman filtering -- Chapter 3. Information theoretic criteria -- Chapter 4. Kalman Filtering Under Information Theoretic Criteria -- Chapter 5. Extended Kalman Filtering Under Information Theoretic Criteria -- Chapter 6. Unscented Kalman Filter Under Information Theoretic Criteria -- Chapter 7. Cubature Kalman Filtering Under Information Theoretic Criteria -- Chapter 8. Additional Topics in Kalman Filtering Under Information Theoretic Criteria 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. 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 HTTP:URL=https://doi.org/10.1007/978-3-031-33764-2

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