The optimality of a class of distributed estimation fusion algorithm

When the measurement noises across sensors at the same time may be correlated, for linear minimum mean-squared errors (LMMSE) estimation, a systematic way to handle the corresponding distributed estimation fusion problem is proposed in this paper based on a unified data model for linear unbiased estimation. The optimality (equivalence to the optimal centralized estimation fusion) of the new optimal distributed estimation fusion algorithm is then analyzed. A necessary and sufficient condition of the optimality for the general case and sufficient conditions for two special cases are given. Comparisons with the existing distributed estimation fusion algorithms are also discussed.

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