A Fast Numerical Method for the Optimal Data Fusion in the Presence of Unknown Correlations

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I recently published a conference paper accessible at IEEEXplore.

Published in: 2018 21st International Conference on Information Fusion (FUSION)


In the presence of unknown correlations, the optimal data fusion, in the sense of Minimum Mean Square Error, can be formulated as a problem of minimizing a nondifferentiable but convex function. The popular projected subgradient methods are known to converge slowly. The single-projection optimal subgradient method, OSGA-V, is known to be numerically more efficient. This paper presents necessary formulations and methods for the application of the OSGA-V algorithm in the minimization of the optimal data fusion problem, achieving much faster convergence rate than the projected subgradient method. We expect this method to significantly reduce the computational cost and time to achieve optimal data fusion in the presence of unknown correlations.

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