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4. Data-Driven Model Set Design For Model Averaged Particle Filter

Data-Driven Model Set Design For Model Averaged Particle Filter

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IEEE ICASSP 2020 Virtual Conference May 2020

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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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