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• W4286306217 abstract "A sequential fusion of multiple partially correlated random vectors is investigated in this paper. The biggest difficulty in dealing with partially correlated random vectors is that the cross covariances are unknown, and this is also challenging for traditional covariance intersection algorithm to solve it in terms of partial correlation. Therefore, the covariance interaction algorithm based on the upper bound of the correlation coefficients of partially correlated random vectors is generalized to the sequential fusion of multiple partially correlated random vectors. The sequential fusion algorithm can be applied to the fusion of measurement information so as to perform estimation of Kalman filter, which generally requires to know the complete cross correlations. Finally, the simulation results show that the proposed sequential fusion algorithm has a tighter upper bound than the traditional covariance interaction." @default.
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• W4286306217 date "2022-05-04" @default.
• W4286306217 modified "2023-10-16" @default.
• W4286306217 title "Sequential Covariance Intersection Based on the Upper Bound of Correlation Coefficient" @default.
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• W4286306217 doi "https://doi.org/10.23919/ascc56756.2022.9828130" @default.
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