Belief-Contraction-Driven Active Inverse Source Localization and Characterization

Abstract:Active inverse source localization and characterization (ISLC) in dynamic fields requires sequential decision making under partial observability, where a mobile sensor must infer latent source parameters from sparse, noisy readings. We introduce a belief-contraction-driven approach that unifies inference, stopping, and control. An attention-augmented particle filter stabilizes Bayesian belief updates through ESS-based resampling, feature-aware sparse attention smoothing, and Metropolis-Hastings rejuvenation that preserves the filtering posterior. Belief contraction (posterior dispersion) defines both a termination rule and a goal-aligned intrinsic reward, enabling reinforcement learning without distance-to-source shaping. Across seven field modalities, spatial out-of-distribution tests, and nonstationary source shifts, our agent (ATT-PFRL) achieves higher completion, faster convergence, and more accurate localization than planning and RL+Bayes baselines under similar computation. Fixed-trajectory studies also show improved ESS and lower RMSE, isolating the benefit of the inference layer.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2501.13084 [cs.LG]

(or arXiv:2501.13084v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2501.13084 Focus to learn more arXiv-issued DOI via DataCite

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