MARC状态:审校 文献类型:西文图书 浏览次数:12
- 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.
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索书号 | 条码号 | 年卷期 | 馆藏地 | 书刊状态 | 还书位置 |
O211.64/5 | B996426 | 库本 库311394 | 可借 | 库本 | |
O211.64/5 | B996425 | 西文阅览室 | 可借 | 西文阅览室 |
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