An Efficient Conditional Score-based Filter for High Dimensional Nonlinear Filtering Problems

• URL: http://arxiv.org/abs/2509.19816v1
• Date: Wed, 24 Sep 2025 06:55:52 GMT
• Title: An Efficient Conditional Score-based Filter for High Dimensional Nonlinear Filtering Problems
• Authors: Zhijun Zeng, Weiye Gan, Junqing Chen, Zuoqiang Shi,
• Abstract summary: Conditional Score-based Filter (CSF) is a novel algorithm that leverages a set-transformer encoder and a conditional diffusion model to achieve efficient and accurate posterior sampling without retraining.<n> experiments on benchmark problems show that CSF achieves superior accuracy, robustness, and efficiency across diverse nonlinear filtering scenarios.
• Score: 6.234365089734548
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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