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Author: Huaiyi Wu
Publisher:
ISBN:
Category : Kalman filtering
Languages : en
Pages : 86
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Book Description
Author: Huaiyi Wu
Publisher:
ISBN:
Category : Kalman filtering
Languages : en
Pages : 86
Get Book Here
Book Description
Author: Guanrong Chen
Publisher: World Scientific
ISBN: 9789810213596
Category : Computers
Languages : en
Pages : 248
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Book Description
Kalman filtering algorithm gives optimal (linear, unbiased and minimum error-variance) estimates of the unknown state vectors of a linear dynamic-observation system, under the regular conditions such as perfect data information; complete noise statistics; exact linear modeling; ideal well-conditioned matrices in computation and strictly centralized filtering.In practice, however, one or more of the aforementioned conditions may not be satisfied, so that the standard Kalman filtering algorithm cannot be directly used, and hence ?approximate Kalman filtering? becomes necessary. In the last decade, a great deal of attention has been focused on modifying and/or extending the standard Kalman filtering technique to handle such irregular cases. It has been realized that approximate Kalman filtering is even more important and useful in applications.This book is a collection of several tutorial and survey articles summarizing recent contributions to the field, along the line of approximate Kalman filtering with emphasis on both its theoretical and practical aspects.
Author: Guanrong Chen
Publisher: World Scientific
ISBN: 9814504351
Category : Technology & Engineering
Languages : en
Pages : 242
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Book Description
Kalman filtering algorithm gives optimal (linear, unbiased and minimum error-variance) estimates of the unknown state vectors of a linear dynamic-observation system, under the regular conditions such as perfect data information; complete noise statistics; exact linear modeling; ideal well-conditioned matrices in computation and strictly centralized filtering.In practice, however, one or more of the aforementioned conditions may not be satisfied, so that the standard Kalman filtering algorithm cannot be directly used, and hence “approximate Kalman filtering” becomes necessary. In the last decade, a great deal of attention has been focused on modifying and/or extending the standard Kalman filtering technique to handle such irregular cases. It has been realized that approximate Kalman filtering is even more important and useful in applications.This book is a collection of several tutorial and survey articles summarizing recent contributions to the field, along the line of approximate Kalman filtering with emphasis on both its theoretical and practical aspects.
Author: Badong Chen
Publisher: Springer Nature
ISBN: 3031337646
Category : Technology & Engineering
Languages : en
Pages : 304
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Book Description
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.
Author: Kumar Pakki Bharani Chandra
Publisher: Springer
ISBN: 3030017974
Category : Technology & Engineering
Languages : en
Pages : 184
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Book Description
This book gives readers in-depth know-how on methods of state estimation for nonlinear control systems. It starts with an introduction to dynamic control systems and system states and a brief description of the Kalman filter. In the following chapters, various state estimation techniques for nonlinear systems are discussed, including the extended, unscented and cubature Kalman filters. The cubature Kalman filter and its variants are introduced in particular detail because of their efficiency and their ability to deal with systems with Gaussian and/or non-Gaussian noise. The book also discusses information-filter and square-root-filtering algorithms, useful for state estimation in some real-time control system design problems. A number of case studies are included in the book to illustrate the application of various nonlinear filtering algorithms. Nonlinear Filtering is written for academic and industrial researchers, engineers and research students who are interested in nonlinear control systems analysis and design. The chief features of the book include: dedicated coverage of recently developed nonlinear, Jacobian-free, filtering algorithms; examples illustrating the use of nonlinear filtering algorithms in real-world applications; detailed derivation and complete algorithms for nonlinear filtering methods, which help readers to a fundamental understanding and easier coding of those algorithms; and MATLAB® codes associated with case-study applications, which can be downloaded from the Springer Extra Materials website.
Author: Charles K. Chui
Publisher: Springer Science & Business Media
ISBN: 3540878483
Category : Business & Economics
Languages : en
Pages : 241
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Book Description
This book presents a thorough discussion of the mathematical theory and computational schemes of Kalman filtering. The filtering algorithms are derived via different approaches, including a direct method and an indirect method.
Author: C. K. Chui
Publisher: Springer
ISBN:
Category : Computers
Languages : en
Pages : 218
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Book Description
Author: Barrie William Leach
Publisher: National Research Council Canada
ISBN:
Category : Airplanes
Languages : en
Pages : 68
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Book Description
Author: I. Bilik
Publisher:
ISBN: 9789533070001
Category :
Languages : en
Pages :
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Book Description
Two new recursive filters, named as GMKF and NL-GMKF, for linear and nonlinear, nonGaussian problems were presented in this chapter. The GMKF algorithm consists of the GSF followed by an efficient model order reduction method. The GSF provides a rigorous solution for state vector estimation in a linear DSS model with GMM-distributed system and measurement noises and it generalizes the original KF to GMMs. Practical implementation of the optimal GSF is limited due to the exponential model order growth. The GMKF solves this problem via an efficient model order reduction method. The problem of exponential growth of the model order was solved via the mixture PDF estimation at each step using the greedy EM algorithm. It was shown that greedy EM-based order reduction scheme does not.
Author: A. V. Balakrishnan
Publisher:
ISBN:
Category : Control theory
Languages : en
Pages : 282
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Book Description
