Dynamic Correction of Erroneous State Estimates via Diffusion Bayesian Exploration

TL;DR

This paper introduces a diffusion-driven Bayesian exploration method to correct early state estimation errors in high-stakes scenarios, overcoming limitations of traditional bootstrap particle filters and improving robustness under misaligned priors.

Contribution

The paper presents a novel diffusion-based Bayesian exploration framework that enables real-time correction of state estimates, addressing the S-PSI problem in bootstrap particle filters.

Findings

Outperforms classical perturbations and RL methods under misaligned priors.

Matches baseline performance when priors are correct.

Provides theoretical guarantees for resolving S-PSI.

Abstract

In emergency response and other high-stakes societal applications, early-stage state estimates critically shape downstream outcomes. Yet, these initial state estimates-often based on limited or biased information-can be severely misaligned with reality, constraining subsequent actions and potentially causing catastrophic delays, resource misallocation, and human harm. Under the stationary bootstrap baseline (zero transition and no rejuvenation), bootstrap particle filters exhibit Stationarity-Induced Posterior Support Invariance (S-PSI), wherein regions excluded by the initial prior remain permanently unexplorable, making corrections impossible even when new evidence contradicts current beliefs. While classical perturbations can in principle break this lock-in, they operate in an always-on fashion and may be inefficient. To overcome this, we propose a diffusion-driven Bayesian exploration framework that enables principled, real-time correction of early state estimation errors. Our method expands posterior support via entropy-regularized sampling and covariance-scaled diffusion. A Metropolis-Hastings check validates proposals and keeps inference adaptive to unexpected evidence. Empirical evaluations on realistic hazardous-gas localization tasks show that our approach matches reinforcement learning and planning baselines when priors are correct. It substantially outperforms classical SMC perturbations and RL-based methods under misalignment, and we provide theoretical guarantees that DEPF resolves S-PSI while maintaining statistical rigor.