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Data-Driven Model Set Design For Model Averaged Particle Filter
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Data-Driven Model Set Design For Model Averaged Particle Filter
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This paper is concerned with sequential state filtering in the presence of nonlinearity, non-Gaussianity and model uncertainty. For this problem, the Bayesian model averaged particle filter (BMAPF) is perhaps one of the most efficient solutions. Major adv
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