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About this paper
Ke Fan, Wen Wu, Haitao Su, Yilin Wang, Yuan Zhou, Chao Zhang
FilterFM presents a single pretrained filter for estimating hidden states from noisy observation streams across systems with different dynamics and dimensions. It combines causal summaries of past observations with detail from the current observation to update state estimates. Across 14 benchmark systems, it averages 0.2322 raw-space RMSE and wins 13, but does not win every system or establish broader generality.
Summary by alphaXiv.
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