Linear Minimum variance estimation fusion

• 作 者: ZHU Yunmin Li Xianrong ZHAO Ju
• 机 构: Department of Mathematics,Department of Electronic Engineering
• 出 处: 中国科学F辑（英文版）
• 发表日期: 2004年期
• 关键词: fusion distributed esimation linear minimum variance estimation
• 基金项目: 国家自然科学基金;国家自然科学基金;ONR;高等学校博士学科点专项科研项目

This paper shows that a general mulitisensor unbiased linearly weighted estimation fusion essentially is the linear minimum variance (LMV) estimation with linear equality constraint, and the general estimation fusion formula is developed by extending the Gauss-Markov estimation to the random paramem of distributed estimation fusion in the LMV setting.In this setting ,the fused estimator is a weighted sum of local estimatess with a matrix quadratic optimization problem subject to a convex linear equality constraint. Second, we present a unique solution to the above optimization problem, which depends only on the covariance matrixCK. Third, if a priori information, the expectation and covariance, of the estimated quantity is unknown, a necessary and sufficient condition for the above LMV fusion becoming the best unbiased LMV estimation with dnown prior information as the above is presented. We also discuss the generality and usefulness of the LMV fusion formulas developed. Finally, we provied and off-line recursion of Ck for a class of multisensor linear systems with coupled measurement noises.