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- 000 04001cam a2200349 i 4500
- 008 200407s2020 cc a b 000 0 eng d
- 020 __ |a 9787030635471 |c CNY156.00
- 040 __ |a HHU |b eng |c HHU |e rda |d SCT
- 050 _4 |a TJ210.2-211.495
- 099 __ |a CAL 022020029541
- 100 1_ |a Ma, Hongbin |9 (马宏宾), |e author.
- 245 10 |a Kalman filtering and information fusion = |b 卡尔曼滤波与信息融合 / |c Hongbin Ma, Liping Yan, Yuanqing Xia, Mengyin Fu.
- 260 __ |a Beijing : |b Science Press ; |a Singpore : |b Springer, |c [2020]
- 300 __ |a xvii, 291 pages : |b illustrations ; |c 24 cm
- 336 __ |a text |b txt |2 rdacontent
- 337 __ |a unmediated |b n |2 rdamedia
- 338 __ |a volume |b nc |2 rdacarrier
- 504 __ |a Includes bibliographical references.
- 505 0_ |a 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.
- 520 __ |a "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.
- 650 _0 |a Kalman filtering.
- 700 1_ |a Yan, Liping, |e author.
- 700 1_ |a Xia, Yuanqing, |e author.
- 700 1_ |a Fu, Mengyin, |e author.