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Kalman filtering under information theoretic criteria

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Kalman filtering under information theoretic criteria/ by Badong Chen ... [et al.].
其他作者:	Chen, Badong.
出版者:	Cham :Springer International Publishing : : 2023.,
面頁冊數:	xv, 294 p. :ill., digital ; : 24 cm.;
Contained By:	Springer Nature eBook
標題:	Artificial Intelligence. -
電子資源:	https://doi.org/10.1007/978-3-031-33764-2
ISBN:	9783031337642

Kalman filtering under information theoretic criteria
Kalman filtering under information theoretic criteria[electronic resource] /by Badong Chen ... [et al.]. - Cham :Springer International Publishing :2023. - xv, 294 p. :ill., digital ;24 cm.

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.

ISBN: 9783031337642

Standard No.: 10.1007/978-3-031-33764-2doiSubjects--Topical Terms:

646849
Artificial Intelligence.

LC Class. No.: QA402.3

Dewey Class. No.: 629.8312

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505 0 $a 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.
520 $a 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.
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