Networked Multisensor Decision and Estimation Fusion Based on Advanced Mathematical Methods
Auteurs : Zhu Yunmin, Zhou Jie, Shen Xiaojing, Song Enbin, Luo Yingting
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.
Introduction. Parallel Statistical Binary Decision Fusion. General Network Statistical Decision Fusion. Some Uncertain Decision Combination. Convex Linear Estimation Fusion. Kalman Filtering Fusion. Robust Estimation Fusion. References.
Date de parution : 07-2012
Ouvrage de 417 p.
15.6x23.4 cm
Thèmes de Networked Multisensor Decision and Estimation Fusion :
Mots-clés :
Interior Point Methods; Dempster’s Combination Rule; Parallel Statistical Binary Decision Fusion; Basic Probability Assignment; General Network Statistical Decision Fusion; Dempster Shafer Evidence Theory; Some Uncertain Decision Combinations; Dempster Shafer Evidences; Convex Linear Estimation Fusion; Fuzzy Set; Kalman Filtering Fusion; Membership Function; Dempster combination rule; Random Set Theory; fuzzy set combination rule; Combination Rule; networked multisensor decision; Random Set; multisensor estimation fusion; Neyman Pearson Test; mathematical methods; Convex Optimization Problem; Fuzzy Membership Function; Dempster’s Rule; Lagrange Dual Problem; Information Fusion; Minimax Decision; Multisource Information; Estimation Fusion; Dempster Shafer Theory; LMI; Lagrange Dual; Multisource Information Fusion; Joint Distribution Function
