山西大同大学图书馆书目检索系统

Online Public Access Catalogue

题名/责任者:

Kalman filtering and information fusion = 卡尔曼滤波与信息融合 / Hongbin Ma, Liping Yan, Yuanqing Xia, Mengyin Fu.

出版发行项:

Beijing : Science Press ; Singpore : Springer, [2020]

ISBN:

9787030635471

载体形态项:

xvii, 291 pages : illustrations ; 24 cm

变异题名:

卡尔曼滤波与信息融合

个人责任者:

Ma, Hongbin author.

附加个人名称:

Yan, Liping, author.

附加个人名称:

Xia, Yuanqing, author.

附加个人名称:

Fu, Mengyin, author.

论题主题:

Kalman filtering.

中图法分类号:

O211.64

书目附注:

Includes bibliographical references.

内容附注:

Part I. Kalman filtering: preliminaries -- 1. Introduction to kalman filtering -- 2. Challenges in kalman filtering -- Part II. Kalman filtering for uncertain systems -- 3. Kalman filter with recursive process noise covariance estimation -- 4. Kalman filter with recursive covariance estimation revisited with technical conditions reduced -- 5. Modified kalman filter with recursive covariance estimation for gyroscope denoising -- 6. Real-time state estimator without noise covariance matrices knowledge -- 7. A framework of finite-model kalman filter with case study: MVDP-FMKF algorithm -- 8. Kalman filters for continuous parametric uncertain systems -- Part III. Kalman filtering for multi-sensor systems -- 9. Optimal centralized, recursive, and distributed fusion for stochastic systems with coupled noises -- 10. Optimal estimation for multirate systems with unreliable measurements and correlated noise -- 11. CKF-based state estimation of nonlinear system by fusion of multirate multisensor unreliable measurements -- Part IV. Kalman filtering for multi-agent systems -- 12. Decentralized adaptive filtering for multi-agent systems with uncertain couplings -- 13. Comparison of several filtering methods for linear multi-agent systems with local unknown parametric couplings.

摘要附注:

"This book addresses a key technology for digital information processing: Kalman filtering, which is generally considered to be one of the greatest discoveries of the 20th century. It introduces readers to issues concerning various uncertainties in a single plant, and to corresponding solutions based on adaptive estimation. Further, it discusses in detail the issues that arise when Kalman filtering technology is applied in multi-sensor systems and/or multi-agent systems, especially when various sensors are used in systems like intelligent robots, autonomous cars, smart homes, smart buildings, etc., requiring multi-sensor information fusion techniques. Furthermore, when multiple agents (subsystems) interact with one another, it produces coupling uncertainties, a challenging issue that is addressed here with the aid of novel decentralized adaptive filtering techniques. Overall, the book's goal is to provide readers with a comprehensive investigation into the challenging problem of making Kalman filtering work well in the presence of various uncertainties and/or for multiple sensors/components. State-of-art techniques are introduced, together with a wealth of novel findings. As such, it can be a good reference book for researchers whose work involves filtering and applications; yet it can also serve as a postgraduate textbook for students in mathematics, engineering, automation, and related fields. To read this book, only a basic grasp of linear algebra and probability theory is needed, though experience with least squares, navigation, robotics, etc. would definitely be a plus."--Back Cover.

豆瓣简介：