无需重训练，高效实现高维非线性滤波的后验采样。
An Efficient Conditional Score-based Filter for High Dimensional Nonlinear Filtering Problems
- 用集合变换器编码器与条件扩散模型解耦先验建模和后验采样
- 在多个基准问题上达到更高精度、更强鲁棒性和更优效率
- 适合需要实时更新的高维非线性系统状态估计任务
在许多工程与应用科学领域，高维非线性滤波仍是挑战性问题。基于得分的扩散模型虽为后验采样提供了有前景的替代方案，但需反复重训以追踪变化的先验，在高维场景下不切实际。本文提出条件得分滤波器（CSF），利用集合变换器编码器与条件扩散模型，实现无需重训的高效且准确的后验采样。通过将先验建模与后验采样分离至离线与在线阶段，CSF可在多种非线性系统中实现可扩展的得分滤波。大量基准测试表明，CSF在多种非线性滤波场景下均展现出卓越的准确性、鲁棒性与效率。
原文摘要 · Abstract (English)
In many engineering and applied science domains, high-dimensional nonlinear filtering is still a challenging problem. Recent advances in score-based diffusion models offer a promising alternative for posterior sampling but require repeated retraining to track evolving priors, which is impractical in high dimensions. In this work, we propose the Conditional Score-based Filter (CSF), a novel algorithm that leverages a set-transformer encoder and a conditional diffusion model to achieve efficient and accurate posterior sampling without retraining. By decoupling prior modeling and posterior sampling into offline and online stages, CSF enables scalable score-based filtering across diverse nonlinear systems. Extensive experiments on benchmark problems show that CSF achieves superior accuracy, robustness, and efficiency across diverse nonlinear filtering scenarios.
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