Robust Linear Estimation Fusion With Allowable Unknown Cross-Covariance
针对局部估计误差间互协方差未知的分布式融合问题，提出一种通过约束互协方差集合的鲁棒融合方法，利用半定规划实现极小极大最优，并给出降低计算量的次优方案。
This paper deals with distributed estimation fusion under unknown cross-covariance between errors of local estimates. We propose a formulation to restrict the set of possible cross-covariance matrices. The constraint in the formulation, named allowance of cross-covariance, provides a flexible way to utilize some prior information on cross-correlation in fusion methods. Then based on the allowance, an optimal robust fusion method is proposed in the minimax sense via semi-definite programming, and suboptimal fusion methods are also discussed to reduce the computational load. We analyze the properties of the proposed fusion methods and describe the relationships between our proposed fusion and some existing fusion methods. Numerical examples are given to illustrate their performance.