@inproceedings{d1a0b40a995049ec98359d203414fc98,
title = "Robust linear estimation fusion with allowable unknown cross-covariance",
abstract = "This paper deals with distributed estimation fusion under unknown cross-covariance between errors of local estimates. We propose a constraint to restrict the set of possible cross-covariance matrices first. Then this constraint, named allowance degree of cross-covariance, is used to derive a fusion method. Based on the allowance degree, we present an optimal robust fusion method in the minimax sense via semi-definite programming and also a suboptimal fusion. We analyze the properties of the proposed fusion methods and describe the relationship between the suboptimal fusion and some existing fusion methods. Numerical examples are given to illustrate their performance compared with the traditional covariance intersection method.",
keywords = "Estimation fusion, covariance intersection, minimax, robust fusion, semi-definite programming",
author = "Yongxin Gao and Li, \{X. Rong\} and Enbin Song",
note = "Publisher Copyright: {\textcopyright} 2014 International Society of Information Fusion.; 17th International Conference on Information Fusion, FUSION 2014 ; Conference date: 07-07-2014 Through 10-07-2014",
year = "2014",
month = oct,
day = "3",
language = "英语",
series = "FUSION 2014 - 17th International Conference on Information Fusion",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "FUSION 2014 - 17th International Conference on Information Fusion",
}