用信念收缩驱动智能体自主定位动态场源，提升精度与效率。
Belief-Contraction-Driven Active Inverse Source Localization and Characterization
- 通过注意力粒子滤波实现稳定贝叶斯推断
- 在7种场模态中完成率更高、收敛更快、误差更低
- 适合需要自适应感知的移动传感任务
动态场中的主动逆源定位与表征（ISLC）需在部分可观测条件下进行序列决策，移动传感器须从稀疏、噪声数据中推断隐含源参数。本文提出一种基于信念收缩的方法，统一了推断、停止与控制。采用注意力增强的粒子滤波器，通过有效样本数（ESS）重采样、特征感知的稀疏注意力平滑及梅特罗波利斯-哈斯廷斯再激活机制，稳定贝叶斯信念更新并保持滤波后验。信念收缩（后验分散度）同时作为终止规则与目标对齐的内在奖励，使强化学习无需依赖距离到源的形状化。在七种场模态、空间分布外测试及非平稳源漂移场景下，所提代理（ATT-PFRL）在计算量相当的情况下，完成率更高、收敛更快、定位更准确，优于规划与强化学习+贝叶斯基线。固定轨迹实验亦显示更高的有效样本数（ESS）与更低的均方根误差（RMSE），凸显推断层的独立贡献。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。