Nonlinear Filtering : Methods and Applications （2019）

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電子版価格
¥21,172

電子版あり

Nonlinear Filtering : Methods and Applications （2019）

Chandra, Kumar Pakki Bharani/ Gu, Da-Wei

ウェブストア価格 ¥27,087（本体¥24,625）
Springer Nature Switzerland AG（2018/12発売）
外貨定価 US$ 129.99
ポイント 246pt

オンデマンド(OD/POD)版です。キャンセルは承れません。

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製本 Hardcover:ハードカバー版／ページ数 184 p.
言語 ENG
商品コード 9783030017965
DDC分類 629.8312

Full 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.

Linear and Nonlinear Control Systems.- State Estimation and Prediction.- Linear Estimation Techniques.- Jacobian-Based Filters.- Unscented Kalman Filters.- Cubature Kalman Filters.- Variants of Cubature Kalman Filters.- Robustness Consideration of Filtering Algorithms.