发表机构
Northwestern University（西北大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究动态系统状态估计问题，提出生成式贝叶斯滤波框架GBF，用预训练条件生成模型取代简单观测模型，经贝叶斯预测-更新递归及转化，实验证明其提升了状态估计的准确性与鲁棒性。
AI 中文摘要
动态系统的状态随时间演变，在控制其可观测行为的几种潜在模式之间切换。滤波方法从观测中推断潜在状态。包括卡尔曼滤波器在内的经典滤波方法通常依赖简单观测模型，无法表征高维传感器信号中日益增加的非线性和异质模式。为应对这一挑战，我们提出生成式贝叶斯滤波（GBF），这是一个滤波框架，用由条件变分自编码器（CVAE）参数化的预训练条件生成模型取代限制性观测模型。对于在线推理，GBF执行贝叶斯预测-更新递归，其中测量更新被公式化为一个后验采样问题，将动态先验与CVAE诱导的似然相结合。然后将由此产生的滤波问题转化为基于分数的采样问题，它自然继承了生成模型的灵活性和集成的不确定性量化能力。在合成数据集以及涉及制造系统监测和心律失常诊断的实际应用上的实验表明，相对于基线方法，GBF提高了状态估计的准确性和鲁棒性。
英文摘要
The state of a dynamic system evolves over time, switching among several latent modes that govern its observable behavior. Filtering methods infer the latent state from observations. Classical filtering approaches, including Kalman filters, typically rely on simple observation models, such as linear-Gaussian models, that are incapable of characterizing the increasingly nonlinear and heterogeneous patterns in high-dimensional sensor signals. To tackle the challenge, we propose Generative Bayesian Filtering (GBF), a filtering framework that replaces restrictive observation models with pretrained conditional generative models parametrized by conditional variational autoencoders (CVAE). For online inference, GBF performs a Bayesian prediction-update recursion in which the measurement update is formulated as a posterior sampling problem that combines the dynamical prior with the CVAE-induced likelihood. The resulting filtering problem is then transformed into a score-based sampling problem, which naturally inherits the flexibility from generative models and the uncertainty quantification capabilities from ensembling. Experiments on synthetic datasets and real-world applications involving manufacturing system monitoring and arrhythmia diagnosis demonstrate that GBF improves state estimation accuracy and robustness relative to baseline approaches.
