发表机构
Duke University（杜克大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对经典声学定位方法在户外声景中易受干扰的问题，提出基于物理信息声学特征的学习模型修正双曲求解器，并提供校准不确定性估计，实现鲁棒的声学定位，助力自动化野生动物监测。
AI 中文摘要
被动声学监测（PAM）的最新进展提供了以前所未有的规模获取生态空间点过程数据的机会。然而，要实现这一机会，需要开发准确且可扩展的定位方法。但在真实的户外声景中，经典定位方法（如双曲定位和基于得分的定位）所基于的假设常因多径主导、近场效应和复杂传播而被违反。在这些条件下，经典定位方法变得脆弱，即使在小型检测阵列中也可能出现极端误差。我们没有从统计上替代底层物理，而是提出了一种方法来完善物理并提高其在理想工作条件之外的鲁棒性：一种基于物理信息声学特征的学习模型，用于修正快速双曲求解器产生的不可信解，大幅减少了灾难性的最坏情况误差，同时在野外数据上的中位数精度与原求解器相当。我们还提供了适用于传播到下游空间模型的、与几何相关的校准不确定性估计。通过在真实和模拟户外环境中的分布式麦克风阵列上进行评估，我们证明所提出的方法能实现鲁棒的、感知不确定性的定位，为在复杂声学环境中实现可扩展的自动化野生动物监测迈出了一步。
英文摘要
Recent advances in Passive Acoustic Monitoring (PAM) offer an opportunity to obtain ecological spatial point-process data at unprecedented scale. However, realizing this opportunity necessitates the development of accurate and scalable localization methods. In real-world outdoor soundscapes, however, the assumptions underlying classical localization methods such as hyperbolic and score-based localization are routinely violated by multipath dominance, near-field effects, and complex propagation. Under these conditions, classical localization methods become brittle, with extreme errors possible even in small detection arrays. Rather than statistically replacing the underlying physics, we propose a method to refine it and increase robustness outside of ideal operating conditions: a learned model operating on physics-informed acoustic features corrects a fast hyperbolic solver where it produces implausible solutions, substantially reducing catastrophic worst-case errors while matching its median accuracy on field data. We further provide calibrated, geometry-aware uncertainty estimates suitable for propagation into downstream spatial models. Evaluating on distributed microphone arrays in real and simulated outdoor environments, we demonstrate that the proposed method yields robust, uncertainty-aware localization, providing a step toward scalable automated wildlife monitoring in complex acoustic environments.
