Generative Model Proposal based Particle Filtering for Data Assimilation

2026-07-01

AI Summary

This work addresses the challenges of particle degeneracy in traditional particle filters and the lack of rigorous Bayesian updating in existing generative approaches when assimilating high-dimensional, nonlinear, non-Gaussian data. To overcome these limitations, the authors propose the Flow-based Proposal Particle Filter (FPPF), which, for the first time, integrates a conditional generative model with computable likelihood into the particle filtering framework. By learning an approximation to the optimal proposal distribution that minimizes variance, FPPF steers particles toward high-likelihood regions and enables exact importance weighting for principled Bayesian updating. A localization strategy is further incorporated to ensure scalability in high-dimensional settings. Experimental results demonstrate that FPPF significantly outperforms both conventional and generative baselines across diverse complex dynamical systems, effectively mitigating particle degeneracy and yielding more accurate and stable posterior estimates.

Abstract

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.

Problem

• data assimilation
• particle filtering
• high-dimensional
• non-linear non-Gaussian
• posterior approximation

Innovation

• particle filtering
• generative model
• data assimilation
• optimal proposal
• localization