Distill-Belief: Closed-Loop Inverse Source Localization and Characterization in Physical Fields

The Distill-Belief framework addresses the challenge of efficient and accurate inverse source localization and characterization (ISLC) for mobile agents by balancing correctness and efficiency. It proposes a teacher-student model, where a Bayes-correct particle filter teacher guides a compact student for fast, uncertainty-aware decision-making in real-time.