What question did this study set out to answer?

The aim is to address the distributed fusion of Gaussian mixture models from local multiple model estimators.

March 25, 2026Open Access

Distributed Fusion of Multiple Model Estimators Using Minimum Forward Kullback-Leibler Divergence Sum

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This article addresses GMM fusion in multiple model estimators, suggesting algorithms that enhance efficiency and accuracy.

The aim is to address the distributed fusion of Gaussian mixture models from local multiple model estimators.
Proposes a distributed fusion framework based on minimum forward Kullback–Leibler divergence sum.
Develops two suboptimal fusion algorithms with closed forms for GMMs within the framework.
Verifies the algorithms' effectiveness through numerical examples.
Both algorithms demonstrated significant computational efficiency.
Improved estimation accuracy was confirmed through numerical simulations.

Wei et al. (2024) studied this question.

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