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Distributed Estimation Fusion with Unavailable Cross-Correlation

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2 Author(s)
Yimin Wang ; Xi''an Jiaotong Univ., Xi''an, China ; Li, X.R.

The problem of distributed fusion for estimation when the cross-correlation of errors of local estimates is unavailable is addressed. We discuss a general estimation fusion approach for this problem-generalized convex combination (GCC) - and classify various GCC fusion approaches in three categories. We develop three GCC fusion algorithms for the problem under consideration. First, based on a set-theoretic formulation of the problem, we propose a relaxed Chebyshev center covariance intersection (RCC-CI) algorithm to fuse the local estimates. Second, based on an information-theoretic criterion, we develop a fast covariance intersection (IT-FCI) algorithm with weights in a closed form. The proposed RCC-CI and IT-FCI algorithms are characterized by both the local estimates and the mean-square error (MSE) matrices being taken into account. Third, to fuse incoherent local estimates, we propose a fault-tolerant GCC fusion algorithm by introducing an adaptive parameter, which can obtain robust fusion and the degree of robustness varies with that of incoherency between estimates to be fused.

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Aerospace and Electronic Systems, IEEE Transactions on  (Volume:48 ,  Issue: 1 )