Networked Multisensor Decision and Estimation Fusion
Language: English
Published by CRC Press, 2012
- Hardcover
- New
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- Title
- Networked Multisensor Decision and Estimation Fusion
- Author
- Yunmin Zhu (Sichuan University, Chengdu, China)|Jie Zhou (Sichuan University, China)|Xiaojing Shen (Sichuan University, China)|Enbin Song (Sichuan University, PR of China)|Yingting Luo (Sichuan University, PR of China)
- Publisher
- CRC Press
- Publication year
- 2012
- Condition
- New
- Binding
- Gebunden
- Language
- English
- ISBN 10
- 1439874522
- ISBN 13
- 9781439874523
Due to the increased capability, reliability, robustness, and survivability of systems with multiple distributed sensors, multi-source information fusion has become a crucial technique in a growing number of areas―including sensor networks, space technology, air traffic control, military engineering, agriculture and environmental engineering, and industrial control. Networked Multisensor Decision and Estimation Fusion: Based on Advanced Mathematical Methods presents advanced mathematical descriptions and methods to help readers achieve more thorough results under more general conditions than what has been possible with previous results in the existing literature.
Examining emerging real-world problems, this book summarizes recent research developments in problems with unideal and uncertain frameworks. It presents essential mathematical descriptions and methods for multisensory decision and estimation fusion. Deriving thorough results under general conditions, this reference book:
- Corrects several popular but incorrect results in this area with thorough mathematical ideas
- Provides advanced mathematical methods, which lead to more general and significant results
- Presents updated systematic developments in both multisensor decision and estimation fusion, which cannot be seen in other existing books
- Includes numerous computer experiments that support every theoretical result
The book applies recently developed convex optimization theory and high efficient algorithms in estimation fusion, which opens a very attractive research subject on minimizing Euclidean error estimation for uncertain dynamic systems. Supplying powerful and advanced mathematical treatment of the fundamental problems, it will help to greatly broaden prospective applications of such developments in practice.
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