TrackDiffuser: Nearly Model-Free Bayesian Filtering with Diffusion Model

2025-02-08

AI Summary

To address the practical challenges of inaccurate state-space models and unknown noise characteristics, this paper proposes a novel Bayesian filtering framework based on conditional diffusion models. The method reformulates state estimation as a conditional generation task, eliminating the need for explicit measurement models or parametric noise priors; instead, it implicitly learns system dynamics from data, achieving model-agnostic inference while preserving the interpretability of classical filters. Its key contribution is the first integration of diffusion models into Bayesian filtering, enabling an end-to-end implicit prediction-update mechanism and a data-driven modeling of the state-observation mapping. Experiments demonstrate that the proposed approach significantly outperforms classical (e.g., EKF, UKF) and hybrid filtering methods under nonlinear dynamics and non-Gaussian noise conditions, and exhibits strong robustness to state-space model mismatch.

Technology Category

• Intelligent Robots: State Estimation
• Reasoning under Uncertainty: Relational Probabilistic Models
• Machine Learning: Bayesian Learning

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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.

Problem

• Addresses state estimation challenges in diverse domains
• Mitigates inaccurate state space model issues
• Eliminates need for explicit noise priors

Innovation

• Uses diffusion model for Bayesian filtering
• Learns system dynamics implicitly from data
• Eliminates need for explicit noise priors