这篇论文提出了一种叫FPPF的新粒子滤波方法，用生成模型做提议分布，解决了高维和非线性场景的退化问题，实验结果很扎实。
论文提出Flow Proposal Particle Filters（FPPF），通过生成模型学习最优提议分布进行粒子传播。FPPF能降低权重方差、延缓退化，并保留贝叶斯更新步骤。通过局部化策略扩展到高维问题。在多个非线性、非高斯、高维动力系统实验中，FPPF优于统计基线和其它生成方法。
Generative Model Proposal based Particle Filtering for Data Assimilation
Data assimilation models state dynamics conditioned on sequential observations, and has wide-ranging scientific applications. In the filtering setting, the goal is to model the posterior over the current state given all observations so far. Classical solutions typically make simplifying distributional or functional assumptions, e.g., linear-Gaussian systems, which can be inaccurate in many scenarios. In principle, particle filters (PFs) remove these assumptions, yet often collapse in high dimensions. Recent generative approaches learn conditional state transitions, but without principled Bayesian updates they do not recover the correct filtering posterior and can accumulate error over long horizons. In this work, we introduce Flow Proposal Particle Filters (FPPF), which learn a conditional generative model based proposal approximating the variance-minimizing optimal proposal for particle propagation. Conditioning on observations steers particles toward high-likelihood regions before weighting, reducing weight variance and delaying degeneracy. Since our proposal admits tractable likelihood evaluation, FPPF computes accurate importance weights and retains a Bayesian update step. We further extend FPPF to high-dimensional problems through localization strategies, adressing another standard PF failure mode. Extensive experiments on a variety of dynamical systems show that FPPF outperforms statistical baselines and other generative methods in non-linear, non-Gaussian, and high-dimensional regimes.