用扩散模型实现无需精确模型的鲁棒状态估计
TrackDiffuser: Nearly Model-Free Bayesian Filtering with Diffusion Model
- 将贝叶斯滤波重构为条件扩散模型，隐式学习系统动态
- 在非高斯噪声下性能显著优于经典与混合方法
- 对状态空间模型误差不敏感，适合真实场景应用
状态估计在自动驾驶、飞行器追踪和量子系统控制等领域仍是核心挑战。尽管贝叶斯滤波是主流方案，但传统基于模型的方法受限于状态空间模型（SSM）不准确及对噪声先验知识依赖。我们提出TrackDiffuser，一种生成式框架，将贝叶斯滤波重构为条件扩散模型。该方法从数据中隐式学习系统动态，缓解了SSM不准确的影响；同时通过建立测量与状态间的直接关系，无需显式测量模型和噪声先验。通过隐式预测-更新机制，保留了传统模型方法的可解释性。大量实验表明，该框架在非线性、非高斯噪声等复杂场景下显著优于经典与现代混合方法。尤其对状态空间模型误差具有极强鲁棒性，为实际应用中缺乏完美模型与先验知识的状态估计提供了实用解法。
原文摘要 · Abstract (English)
State estimation remains a fundamental challenge across numerous domains, from autonomous driving, aircraft tracking to quantum system control. Although Bayesian filtering has been the cornerstone solution, its classical model-based paradigm faces two major limitations: it struggles with inaccurate state space model (SSM) and requires extensive prior knowledge of noise characteristics. We present TrackDiffuser, a generative framework addressing both challenges by reformulating Bayesian filtering as a conditional diffusion model. Our approach implicitly learns system dynamics from data to mitigate the effects of inaccurate SSM, while simultaneously circumventing the need for explicit measurement models and noise priors by establishing a direct relationship between measurements and states. Through an implicit predict-and-update mechanism, TrackDiffuser preserves the interpretability advantage of traditional model-based filtering methods. Extensive experiments demonstrate that our framework substantially outperforms both classical and contemporary hybrid methods, especially in challenging non-linear scenarios involving non-Gaussian noises. Notably, TrackDiffuser exhibits remarkable robustness to SSM inaccuracies, offering a practical solution for real-world state estimation problems where perfect models and prior knowledge are unavailable.
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